Journal articles: 'Retrieval systems'

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Tokumi, Yuta, Junki Hakamata, and Masataka Tokumaru. "Development of a Nutritional Management System for a Healthy Eating Habits Support System." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (March 20, 2013): 324–34. http://dx.doi.org/10.20965/jaciii.2013.p0324.

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In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.

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Crook, D. A., B. J. Adair, and L. J. Hetherington. "Low-cost device for retrieval of moorings deployed with underwater remote release systems." Marine and Freshwater Research 69, no. 3 (2018): 473. http://dx.doi.org/10.1071/mf17189.

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Underwater acoustic and timed-release systems are commonly deployed with sensors and other equipment for ecological and oceanographic research in aquatic environments. These systems allow equipment deployed at depth to be remotely released from their moorings for retrieval. However, a current limitation is that most systems do not incorporate mechanisms for retrieval of the mooring anchor and attachments (e.g. shackles, cable). Herein we provide a detailed description and evaluation of a ‘retrieval pod’ that can be attached directly to remotely released underwater devices to facilitate mooring retrieval. The system was tested using acoustic release telemetry receivers under field conditions in a large estuary in the Northern Territory, Australia. Six receivers fitted with pods were retrieved from the field after two consecutive 6-month deployments (12 retrievals in total) with no failed mooring retrievals. Although our tests were confined to shallow depths (<8m), it should be straightforward to modify the system for use in deeper water by increasing the spool capacity. The retrieval pods are reusable, require minimal construction skills and can be assembled from basic equipment available at most hardware or boating stores, making them a convenient and low-cost option for the retrieval of moorings deployed with remote release systems.

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Orscheschek, Franziska, Tilo Strobach, Torsten Schubert, and Timothy Rickard. "Two retrievals from a single cue: A bottleneck persists across episodic and semantic memory." Quarterly Journal of Experimental Psychology 72, no. 5 (May 28, 2018): 1005–28. http://dx.doi.org/10.1177/1747021818776818.

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There is evidence in the literature that two retrievals from long-term memory cannot occur in parallel. To date, however, that work has explored only the case of two retrievals from newly acquired episodic memory. These studies demonstrated a retrieval bottleneck even after dual-retrieval practice. That retrieval bottleneck may be a global property of long-term memory retrieval, or it may apply only to the case of two retrievals from episodic memory. In the current experiments, we explored whether that apparent dual-retrieval bottleneck applies to the case of one retrieval from episodic memory and one retrieval from highly overlearned semantic memory. Across three experiments, subjects learned to retrieve a left or right keypress response form a set of 14 unique word cues (e.g., black—right keypress). In addition, they learned a verbal response which involved retrieving the antonym of the presented cue (e.g., black—“white”). In the dual-retrieval condition, subjects had to retrieve both the keypress response and the antonym word. The results suggest that the retrieval bottleneck is superordinate to specific long-term memory systems and holds across different memory components. In addition, the results support the assumption of a cue-level response chunking account of learned retrieval parallelism.

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Abubacker, K. A. Shaheer, J. Sutha, and K. A. Shahul Hameed. "A simple multi-feature based stereoscopic medical image retrieval system." Polish Journal of Medical Physics and Engineering 25, no. 2 (June 1, 2019): 127–30. http://dx.doi.org/10.2478/pjmpe-2019-0017.

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Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.

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Luo, Youmeng, Wei Li, Xiaoyu Ma, and Kaiqiang Zhang. "Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism." Information 13, no. 10 (September 24, 2022): 446. http://dx.doi.org/10.3390/info13100446.

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With the continuous progress of image retrieval technology, in the field of image retrieval, the speed of a search for a desired image from a great deal of image data becomes a hot issue. Convolutional Neural Networks(CNN) have been used in the field of image retrieval. However, many image retrieval systems based on CNN have a poor ability to express image features, resulting in a series of problems such as low retrieval accuracy and robustness. When the target image is retrieved from a large amount of image data, the vector dimension after image coding is high and the retrieval efficiency is low. Locality-sensitive hash is a method to find similar data from massive high latitude data. It reduces the data dimension of the original spatial data through hash coding and conversion, and can also maintain the similarity between the data. The retrieval time and space complexity are low. Therefore, this paper proposes a locality-sensitive hash image retrieval method based on CNN and the attention mechanism. The steps of the method are as follows: using the ResNet50 network as the feature extractor of the image, adding the attention module after the convolution layer of the model, and using the output of the network full connection layer to retrieve the features of the image database, then using the local-sensitive hash algorithm to hash code the image features of the database to reduce the dimension and establish the index, and finally measuring the features of the image to be retrieved and the image database to get the most similar image, completing the content-based image retrieval task. The method in this paper is compared with other image retrieval methods on corel1k and corel5k datasets. The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved. It also has higher robustness in different scenarios.

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Tseng, Chien-Hao, Chia-Chien Hsieh, Dah-Jing Jwo, Jyh-Horng Wu, Ruey-Kai Sheu, and Lun-Chi Chen. "Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation." Journal of Sensors 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/9566628.

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Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.

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D, Lande, Soboliev A, and Dmytrenko O. "Intelligent technologies in information retrieval systems." Artificial Intelligence 27, jai2022.27(1) (June 20, 2022): 260–68. http://dx.doi.org/10.15407/jai2022.01.260.

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This paper considers the use of modern intelligent technologies in information retrieval systems. A general scheme for the implementation of Internet search engines is presented. The existing and prospective approaches to the intellectualization of individual components of this scheme are presented. An approach to the creation of a system of intelligent agents for information collection is presented. These agents are combined into teams and exchange the results of their work with each other. They form a reliable basis for the information base of search engines, ensure uninterrupted operation of the system in case of failure of individual agents. Methods for the formation of semantic networks corresponding to the texts of individual documents are also considered. These networks are considered as search patterns of documents for information retrieval and detection of duplicates or similar documents. Machine learning methods are used to conduct sentiment analysis. The paper describes an approach that made it possible to make the transition from the use of a naive Bayesian model to a modern machine learning system. The issues of cluster analysis and visualization of search results are also considered.

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S, Sangeetha, and Srividhya S.R. "Deconstructing Information Retrieval Systems with Plumbara." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 868–74. http://dx.doi.org/10.5373/jardcs/v11/20192645.

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Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm." September 2020 02, no. 03 (August 28, 2020): 151–60. http://dx.doi.org/10.36548/jitdw.2020.3.003.

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The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.

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Enser, P. G. B. "Document retrieval systems." International Journal of Information Management 10, no. 2 (June 1990): 156–57. http://dx.doi.org/10.1016/0268-4012(90)90009-h.

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Arunarani, Ar, and D. Manjula Perkinian. "Intelligent Techniques for Providing Effective Security to Cloud Databases." International Journal of Intelligent Information Technologies 14, no. 1 (January 2018): 1–16. http://dx.doi.org/10.4018/ijiit.2018010101.

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Cloud databases have been used in a spate of web-based applications in recent years owing to their capacity to store big data efficiently. In such a scenario, access control techniques implemented in relational databases are so modified as to suit cloud databases. The querying features of cloud databases are designed with facilities to retrieve encrypted data. The performance with respect to retrieval and security needs further improvements to ensure a secured retrieval process. In order to provide an efficient secured retrieval mechanism, a rule- and agent-based intelligent secured retrieval model has been proposed in this paper that analyzes the user, query and contents to be retrieved so as to effect rapid retrieval with decryption from the cloud databases. The major advantage of this retrieval model is in terms of its improved query response time and enhanced security of the storage and retrieval system. From the experiments conducted in this work, proposed model increased storage and access time and, in addition, intensified the security of the data stored in cloud databases.

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Anandh, A., K. Mala, and R. Suresh Babu. "Combined global and local semantic feature–based image retrieval analysis with interactive feedback." Measurement and Control 53, no. 1-2 (December 12, 2019): 3–17. http://dx.doi.org/10.1177/0020294018824122.

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Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

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Liu, Hongfang, Manabu Torii, Guixian Xu, and Johannes Goll. "Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval." International Journal of Computational Models and Algorithms in Medicine 1, no. 1 (January 2010): 34–44. http://dx.doi.org/10.4018/jcmam.2010072003.

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Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used.

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Chimah, Jonathan N., and Friday Ibiam Ude. "Current trends in information retrieval systems: review of fuzzy set theory and fuzzy Boolean retrieval models." Journal of Library Services and Technologies 2, no. 2 (June 2020): 48–56. http://dx.doi.org/10.47524/jlst.v2i2.5.

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This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, can be used to expand the set of the documents retrieved in response to a query.The paper concludesby recommending that in an electronic library environment, the librarians and information scientists should acquaint themselves with these terms in order to be more equipped in helping library users retrieve online documents relevant to their information needs.

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Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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Norquist, Donald C., Paul R. Desrochers, Patrick J. McNicholl, and John R. Roadcap. "A Characterization of Cirrus Cloud Properties That Affect Laser Propagation." Journal of Applied Meteorology and Climatology 47, no. 5 (May 1, 2008): 1322–36. http://dx.doi.org/10.1175/2007jamc1756.1.

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Abstract Future high-altitude laser systems may be affected by cirrus clouds. Laser transmission models were applied to measured and retrieved cirrus properties to determine cirrus impact on power incident on a target or receiver. A major goal was to see how well radiosondes and geostationary satellite imagery could specify the required properties. Based on the use of ground-based radar and lidar measurements as a reference, errors in cirrus-top and cirrus-base height estimates from radiosonde observations were 20%–25% of geostationary satellite retrieval errors. Radiosondes had a perfect cirrus detection rate as compared with 80% for satellite detection. Ice water path and effective particle size were obtained with a published radar–lidar retrieval algorithm and a documented satellite algorithm. Radar–lidar particle size and ice water path were 1.5 and 3 times the satellite retrievals, respectively. Radar–lidar-based laser extinction coefficients were 55% greater than satellite values. Measured radar–lidar cirrus thickness was consistently greater than satellite-retrieved thickness, but radar–lidar microphysical retrieval required detection by both sensors at each range gate, which limited the retrievals’ vertical extent. Greater radar–lidar extinction and greater satellite-based cirrus thickness yielded comparable optical depths for the two independent retrievals. Laser extinction–transmission models applied to radiosonde-retrieved cirrus heights and satellite-retrieved microphysical properties revealed a significant power loss by all models as the laser beam transits the cirrus layer. This suggests that cirrus location is more important than microphysics in high-altitude laser test support. Geostationary satellite imagery may be insufficient in cirrus detection and retrieval accuracy. Humidity-sensitive radiosondes are a potential proxy for ground-based remote sensors in cirrus detection and altitude determination.

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Visschedijk, Ankie, and Forbes Gibb. "UNCONVENTIONAL TEXT RETRIEVAL SYSTEMS." Online and CD-Rom Review 17, no. 1 (January 1993): 11–23. http://dx.doi.org/10.1108/eb024418.

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Roy, Sujoy, and Ee-Chien Chang. "Watermarking with retrieval systems." Multimedia Systems 9, no. 5 (March 1, 2004): 433–40. http://dx.doi.org/10.1007/s00530-003-0116-1.

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Pillay, Karan Ravindran, and Omkar Upendra Khadilkar. "The Scalable Image Retrieval Systems and Applications." International Journal of Engineering and Computer Science 7, ``11 (November 13, 2018): 24406–8. http://dx.doi.org/10.18535/ijecs/v7i11.03.

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Advances in information storage and image acquisition technologies have enabled the creation of enormous image datasets. during this situation, it's necessary to develop applicable data systems to with efficiency manage these collections. the most typical approaches use the supposed Content-Based Image Retrieval (CBIR) systems. Basically, these systems attempt to retrieve pictures like a user-defined specification or pattern (e.g., form sketch, image example). Their goal is to support image retrieval supported content properties (e.g., shape, color, texture), typically encoded into feature vectors. one among the most benefits of the CBIR approach is that the chance of AN automatic retrieval method, rather than the standard keyword-based approach, thattypically needs terribly toilsome and long previous annotation of info pictures. The CBIR technology has been utilized in many applications like fingerprint identification, variety data systems, digital libraries, crime bar, medicine, historical analysis, among others.

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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (December 1, 2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.

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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

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Tu, Jinsheng, Haohan Wei, Rui Zhang, Lei Yang, Jichao Lv, Xiaoming Li, Shihai Nie, Peng Li, Yanxia Wang, and Nan Li. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data." Remote Sensing 13, no. 21 (October 26, 2021): 4311. http://dx.doi.org/10.3390/rs13214311.

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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

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Gao, Meng, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, et al. "Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean." Atmospheric Measurement Techniques 15, no. 16 (August 25, 2022): 4859–79. http://dx.doi.org/10.5194/amt-15-4859-2022.

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Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

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Patel, Rahul S., Gajanan P. Khapre, and R. M. Mulajkr. "Video Retrieval Systems Methods, Techniques, Trends and Challenges." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 72–81. http://dx.doi.org/10.31142/ijtsrd5862.

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Chang, Yuyang, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, and Thierry Podvin. "Retrieval of Aerosol Microphysical Properties from Multi-Wavelength Mie–Raman Lidar Using Maximum Likelihood Estimation: Algorithm, Performance, and Application." Remote Sensing 14, no. 24 (December 7, 2022): 6208. http://dx.doi.org/10.3390/rs14246208.

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Lidar plays an essential role in monitoring the vertical variation of atmospheric aerosols. However, due to the limited information that lidar measurements provide, ill-posedness still remains a big challenge in quantitative lidar remote sensing. In this study, we describe the Basic algOrithm for REtrieval of Aerosol with Lidar (BOREAL), which is based on maximum likelihood estimation (MLE), and retrieve aerosol microphysical properties from extinction and backscattering measurements of multi-wavelength Mie–Raman lidar systems. The algorithm utilizes different types of a priori constraints to better constrain the solution space and suppress the influence of the ill-posedness. Sensitivity test demonstrates that BOREAL could retrieve particle volume size distribution (VSD), total volume concentration (Vt), effective radius (Reff), and complex refractive index (CRI = n − ik) of simulated aerosol models with satisfying accuracy. The application of the algorithm to real aerosol events measured by LIlle Lidar AtmosphereS (LILAS) shows it is able to realize fast and reliable retrievals of different aerosol scenarios (dust, aged-transported smoke, and urban aerosols) with almost uniform and simple pre-settings. Furthermore, the algorithmic principle allows BOREAL to incorporate measurements with different and non-linearly related errors to the retrieved parameters, which makes it a flexible and generalized algorithm for lidar retrieval.

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Zakariya, S. M., and Imtiaz A. Khan. "Analysis of combined approaches of CBIR systems by clustering at varying precision levels." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5009. http://dx.doi.org/10.11591/ijece.v11i6.pp5009-5018.

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<span lang="EN-US">The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and color-texture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named cluster-based retrieval of images by unsupervised learning (CLUE).</span>

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Rorissa, Abebe. "Image retrieval: Benchmarking visual information indexing and retrieval systems." Bulletin of the American Society for Information Science and Technology 33, no. 3 (September 11, 2008): 15–17. http://dx.doi.org/10.1002/bult.2007.bult1720330310.

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Sussmann, R., and T. Borsdorff. "Technical Note: Interference errors in infrared remote sounding of the atmosphere." Atmospheric Chemistry and Physics 7, no. 13 (July 6, 2007): 3537–57. http://dx.doi.org/10.5194/acp-7-3537-2007.

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Abstract. Classical error analysis in remote sounding distinguishes between four classes: "smoothing errors," "model parameter errors," "forward model errors," and "retrieval noise errors". For infrared sounding "interference errors", which, in general, cannot be described by these four terms, can be significant. Interference errors originate from spectral residuals due to "interfering species" whose spectral features overlap with the signatures of the target species. A general method for quantification of interference errors is presented, which covers all possible algorithmic implementations, i.e., fine-grid retrievals of the interfering species or coarse-grid retrievals, and cases where the interfering species are not retrieved. In classical retrieval setups interference errors can exceed smoothing errors and can vary by orders of magnitude due to state dependency. An optimum strategy is suggested which practically eliminates interference errors by systematically minimizing the regularization strength applied to joint profile retrieval of the interfering species. This leads to an interfering-species selective deweighting of the retrieval. Details of microwindow selection are no longer critical for this optimum retrieval and widened microwindows even lead to reduced overall (smoothing and interference) errors. Since computational power will increase, more and more operational algorithms will be able to utilize this optimum strategy in the future. The findings of this paper can be applied to soundings of all infrared-active atmospheric species, which include more than two dozen different gases relevant to climate and ozone. This holds for all kinds of infrared remote sounding systems, i.e., retrievals from ground-based, balloon-borne, airborne, or satellite spectroradiometers.

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Wei, Yi-Peng, Batuhan Arasli, Karim Banawan, and Sennur Ulukus. "The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases." Information 10, no. 12 (November 28, 2019): 372. http://dx.doi.org/10.3390/info10120372.

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We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint μ K L bits exist in the system. Each database independently chooses μ K L bits out of the total K L bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be D * = ∑ n = 1 N + 1 N n - 1 μ n - 1 ( 1 - μ ) N + 1 - n 1 + 1 n + ⋯ + 1 n K - 1 . We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly results in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar, and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions.

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NNEBE, Samuel Ekene, Efeosasere Okoro, Fatai Idowu SADIQ, and Benjamin A. ABARA. "A User-Centric Multi-Context Hybrid Reasoning Information Retrieval Model." International Journal of Innovative Computing 12, no. 1 (November 16, 2021): 43–54. http://dx.doi.org/10.11113/ijic.v12n1.337.

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Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.

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Qiao, Ya-nan, Qinghe Du, and Di-fang Wan. "A study on query terms proximity embedding for information retrieval." International Journal of Distributed Sensor Networks 13, no. 2 (February 2017): 155014771769489. http://dx.doi.org/10.1177/1550147717694891.

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Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it is a very useful information to improve the performance of information retrieval systems. Query terms proximity cannot retrieve documents independently, and it must be incorporated into original information retrieval models. This article proposes the concept of query term proximity embedding, which is a new method to incorporate query term proximity into original information retrieval models. Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.

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Sharma, Prem Sagar, and Divakar Yadav. "Incremental Refinement of Page Ranking of Web Pages." International Journal of Information Retrieval Research 10, no. 3 (July 2020): 57–73. http://dx.doi.org/10.4018/ijirr.2020070104.

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Web-based information retrieval systems called search engines have made things easy for information seekers, but still do not provide guarantees about the relevance of the information provided to the users. Information retrieval systems provide the information to the user based on certain retrieval criteria. Due to the large size of the WWW, it is very common that a large number of documents get identified related to a particular domain. Therefore, to help users towards finding the best matching documents, a ranking mechanism is employed by the search engine. In this article, an improved architecture for an information retrieval system is proposed. The proposed system makes a query log for each user query and stores the results retrieved to the user for that query. The system also provides relevant results by analyzing the content of the pages retrieved for the user query.

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Rajagopal, Prabha, Taoufik Aghris, Fatima-Ezzahra Fettah, and Sri Devi Ravana. "Clustering of Relevant Documents Based on Findability Effort in Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (January 6, 2023): 1–18. http://dx.doi.org/10.4018/ijirr.315764.

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A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.

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Wu, Yanjun, Xinnian Wang, and Tao Zhang. "Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images." Information 10, no. 2 (January 30, 2019): 45. http://dx.doi.org/10.3390/info10020045.

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Given a query shoeprint image, shoeprint retrieval aims to retrieve the most similar shoeprints available from a large set of shoeprint images. Most of the existing approaches focus on designing single low-level features to highlight the most similar aspects of shoeprints, but their retrieval precision may vary dramatically with the quality and the content of the images. Therefore, in this paper, we proposed a shoeprint retrieval method to enhance the retrieval precision from two perspectives: (i) integrate the strengths of three kinds of low-level features to yield more satisfactory retrieval results; and (ii) enhance the traditional distance-based similarity by leveraging the information embedded in the neighboring shoeprints. The experiments were conducted on a crime scene shoeprint image dataset, that is, the MUES-SR10KS2S dataset. The proposed method achieved a competitive performance, and the cumulative match score for the proposed method exceeded 92.5% in the top 2% of the dataset, which was composed of 10,096 crime scene shoeprints.

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34

Patel, B. V. "Content Based Video Retrieval Systems." International Journal of UbiComp 3, no. 2 (April 30, 2012): 13–30. http://dx.doi.org/10.5121/iju.2012.3202.

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35

Paice, Chris. "Expert systems for information retrieval?" Aslib Proceedings 38, no. 10 (October 1986): 343–53. http://dx.doi.org/10.1108/eb051026.

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36

Zuva, Keneilwe. "Evaluation of Information Retrieval Systems." International Journal of Computer Science and Information Technology 4, no. 3 (June 30, 2012): 35–43. http://dx.doi.org/10.5121/ijcsit.2012.4304.

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37

Salton, Gerard. "Expert systems and information retrieval." ACM SIGIR Forum 31, no. 1 (April 1997): 39–42. http://dx.doi.org/10.1145/263868.263873.

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38

Salton, G. "Expert systems and information retrieval." ACM SIGIR Forum 21, no. 3-4 (March 1987): 3–9. http://dx.doi.org/10.1145/30075.30076.

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39

Harman, Donna, Chris Buckley, Jamie Callan, Susan Dumais, David Lewis, Steve Robertson, Alan Smeaton, Karen Sparck Jones, and Richard Tong. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417.c.

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Last, Jay T. "Electronic Imaging and Retrieval Systems." Popular Culture in Libraries 4, no. 2 (June 25, 1997): 25–34. http://dx.doi.org/10.1300/j117v04n02_03.

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41

Locatis, Craig. "Information Retrieval Systems and Learning." Performance Improvement Quarterly 2, no. 3 (October 22, 2008): 4–15. http://dx.doi.org/10.1111/j.1937-8327.1989.tb00408.x.

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Seiden, Peggy, and Mark Kibbey. "Information Retrieval Systems for Microcomputers." Library Hi Tech 3, no. 1 (January 1985): 41–54. http://dx.doi.org/10.1108/eb047581.

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Harman, D., C. Buckley, J. Callan, S. Dumais, D. Lewis, S. Robertson, A. Smeaton, K. S. Jones, and R. Tong. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1417–18. http://dx.doi.org/10.1126/science.268.5216.1417-b.

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44

Salton, G. "Performance of Text Retrieval Systems." Science 268, no. 5216 (June 9, 1995): 1418–19. http://dx.doi.org/10.1126/science.268.5216.1418.

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Grant-Thomson, John, A. Douglas, and J. Hilton. "Neonatal retrieval systems down under." Air Medical Journal 19, no. 4 (October 2000): 150. http://dx.doi.org/10.1016/s1067-991x(00)90017-8.

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Araghi, Gholamreza Fadaie. "Major Problems in Retrieval Systems." Cataloging & Classification Quarterly 40, no. 1 (May 2005): 43–53. http://dx.doi.org/10.1300/j104v40n01_04.

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YAO, Y. "Neighborhood systems and approximate retrieval." Information Sciences 176, no. 23 (December 4, 2006): 3431–52. http://dx.doi.org/10.1016/j.ins.2006.02.002.

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Rashmi, Ms. "Introduction to Information Retrieval Systems." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2051–54. http://dx.doi.org/10.17762/ijritcc2321-8169.150462.

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Gudivada, V. N., and V. V. Raghavan. "Content based image retrieval systems." Computer 28, no. 9 (1995): 18–22. http://dx.doi.org/10.1109/2.410145.

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Everett, Daniel M., and Steven C. Cater. "Topology of document retrieval systems." Journal of the American Society for Information Science 43, no. 10 (December 1992): 658–73. http://dx.doi.org/10.1002/(sici)1097-4571(199212)43:10<658::aid-asi3>3.0.co;2-h.

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Journal articles on the topic 'Retrieval systems'

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