Relevant bibliographies by topics / Image retrieval

Academic literature on the topic 'Image retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Image retrieval"

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LEE, SUH-YIN, and MAN-KWAN SHAN. "ACCESS METHODS OF IMAGE DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 27–44. http://dx.doi.org/10.1142/s0218001490000034.

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The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve images in an image database system. The data structure called 2-D string, proposed by Chang et al., is adopted to represent the symbolic pictures. When there are a large number of images in the image database and each image contains many objects, the processing time for image retrievals is tremendous. It is essential to develop efficient access methods for these retrievals. In this paper, the efficient methods for retrieval by objects, retrieval by pairwise spatial relationships and retrieval by subpicture are proposed. All the methods are based on the superimposed coding technique.
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Shiral, J. V., Munmun Burman, Apurva Bhadbhade, Dhanashree Patil, Kajal Motghare, and Neha Wanjari. "Retrieval of Images Using SVM." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (March 31, 2015): 106–11. http://dx.doi.org/10.53555/nncse.v2i3.500.

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Image retrieval is a technique which is used to search and retrieve images from a large database of digital images. Content-based image retrieval (CBIR) is a technique which allows searching images from large scale image database based on contents as needed by user.This paper introduces a technique to retrieve images by classifying it on the basis of the features and characteristics it contains using Support Vector Machine (SVM). The dataset of images is created which is used for feature matching purpose by SVM to find similar images from the database and based on user requirements images are retrieved.
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Mustikasari, Metty, and Sarifuddin Madenda. "Performance Analysis of Color based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 4 (January 20, 2014): 3373–81. http://dx.doi.org/10.24297/ijct.v12i4.7058.

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Recently Content based image retrieval (CBIR) is an active research. This paper proposes a technique to retrieve images based on color feature and evaluate the retrieval system performance. In this retrieval system Euclidean distance and City block distance are used to measure similarity of images. This algorithm is tested by using Corel image database which is provided by James Wang. The performance of retrieval system is measured in terms of its recall and precision. The effectiveness of retrieval system is also measured based on Average Rank (AVRR) of all relevant retrieves images and Ideal Average Rank of relevant images (IAVRR). The experimental results show that city block has achieved higher retrieval performance than Euclidean distance.
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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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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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Li, Quan. "A Partitioning Image Retrieval Method Based on Regional Division and Polymerization." Applied Mechanics and Materials 347-350 (August 2013): 2218–22. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2218.

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in order to further improve the retrieval accuracy of the image retrieval system based on shape feature. A partitioning image retrieval method based on regional division and polymerization is proposed in this paper. Firstly, an image is segmented by the regional division and polymerization method. Secondly, the shape and spatial features of different objects in the image are extracted by the invariant moment. Finally, images are retrieved by calculating the similarity of images, and five different types of images are tested by group. The experimental results show that it is more accurate for the algorithm to retrieve the users interested images.
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Li, Zhongjian, Jun Xiang, Lei Wang, Ning Zhang, Ruru Pan, and Weidong Gao. "Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm." Fibres and Textiles in Eastern Europe 27, no. 5(137) (October 31, 2019): 60–69. http://dx.doi.org/10.5604/01.3001.0013.2900.

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Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.
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Xie, Dan, and Chao Yin. "Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology." PeerJ Computer Science 9 (April 18, 2023): e1330. http://dx.doi.org/10.7717/peerj-cs.1330.

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Image retrieval technology has emerged as a popular research area of China’s development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network’s capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system’s picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.
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Gao, Fei. "Rapid Feature Retrieval Method in Large-Scale Image Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (November 20, 2018): 1088–92. http://dx.doi.org/10.20965/jaciii.2018.p1088.

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The retrieval of features in a large-scale image database can improve the degree of visualization of images. The conventional method of feature-retrieval is a time-consuming process because it retrieves by searching the keywords. In this paper, a rapid feature retrieval method based on granular computing is proposed for use in a large-scale image database. In this method, we first collect and process the images from the database. Next, we construct a binary tree to realize the multi-class classification of the image features and complete the feature retrieval using support vector machines. The experimental results demonstrate that the proposed method can effectively retrieve the features in the large-scale image database. The effectiveness of retrieval can reach more than 95%.
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Gupta, Rajeev, and Virender Singh. "COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE." International Journal of Students' Research in Technology & Management 8, no. 1 (January 26, 2020): 01–10. http://dx.doi.org/10.18510/ijsrtm.2020.811.

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Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace. Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time. Main Findings: This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query. Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace. Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.
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Dissertations / Theses on the topic "Image retrieval"

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Ahmad, Fauzi Mohammad Faizal. "Content-based image retrieval of museum images." Thesis, University of Southampton, 2004. https://eprints.soton.ac.uk/261546/.

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Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections.
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Gibson, Stuart Edward. "Sieves for image retrieval." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405401.

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Nahar, Vikas. "Content based image retrieval for bio-medical images." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Nahar_09007dcc80721e0b.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed Dec. 23, 2009). Includes bibliographical references (p. 82-83).
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Saavedra, Rondo José Manuel. "Image Descriptions for Sketch Based Image Retrieval." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/112670.

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Doctor en Ciencias, Mención Computación
Debido al uso masivo de Internet y a la proliferación de dispositivos capaces de generar información multimedia, la búsqueda y recuperación de imágenes basada en contenido se han convertido en áreas de investigación activas en ciencias de la computación. Sin embargo, la aplicación de búsqueda por contenido requiere una imagen de ejemplo como consulta, lo cual muchas veces puede ser un problema serio, que imposibilite la usabilidad de la aplicación. En efecto, los usuarios comúnmente hacen uso de un buscador de imágenes porque no cuentan con la imagen deseada. En este sentido, un modo alternativo de expresar lo que el usuario intenta buscar es mediante un dibujo a mano compuesto, simplemente, de trazos, sketch, lo que onduce a la búsqueda por imágenes basada en sketches. Hacer este tipo de consultas es soportado, además, por el hecho de haberse incrementado la accesibilidad a dispositivos táctiles, facilitando realizar consultas de este tipo. En este trabajo, se proponen dos métodos aplicados a la recuperación de imágenes basada en sketches. El primero es un método global que calcula un histograma de orientaciones usando gradientes cuadrados. Esta propuesta exhibe un comportamiento sobresaliente con respecto a otros métodos globales. En la actualidad, no existen métodos que aprovechen la principal característica de los sketches, la información estructural. Los sketches carecen de color y textura y representan principalmente la estructura de los objetos que se quiere buscar. En este sentido, se propone un segundo método basado en la representación estructural de las imágenes mediante un conjunto de formas primitivas que se denominan keyshapes. Los resultados de nuestra propuesta han sido comparados con resultados de métodos actuales, mostrando un incremento significativo en la efectividad de la recuperación. Además, puesto que nuestra propuesta basada en keyshapes explota una característica novedosa, es posible combinarla con otras técnicas para incrementar la efectividad de los resultados. Así, en este trabajo se ha evaluado la combinación del método propuesto con el método propuesto por Eitz et al., basado en Bag of Words, logrando un aumento de la efectividad de casi 22%. Finalmente, con el objetivo de mostrar el potencial de nuestra propuesta, se muestran dos aplicaciones. La primera está orientada al contexto de recuperación de modelos 3D usando un dibujo a mano como consulta. En esta caso, nuestros resultados muestran competitividad con el estado del arte. La segunda aplicación explota la idea de buscar objetos basada en la estructura para mejorar el proceso de segmentación. En particular, mostramos una aplicación de segmentación de manos en ambientes semi-controlados.
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Ingratta, Donato. "Texture image retrieval using fuzzy image subdivision." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/MQ52743.pdf.

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Ren, Feng Hui. "Multi-image query content-based image retrieval." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070103.143624/index.html.

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Nanayakkara, Wasam Uluwitige Dinesha Chathurani. "Content based image retrieval with image signatures." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104286/1/Dinesha_Chathurani_Nanayakkara_Thesis.pdf.

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This thesis develops a system to search for relevant images when user inputs a particular image as a query. The concept is similar to text search in Google or Yahoo. However, understanding image content is more difficult than text content. The system provides a method to retrieve similar images pertaining to the query easily and quickly. It allows end users to refine the original query iteratively where they have no effective way to reformulate the original image query. The results from empirical evaluations suggest that our system is fast, provides a broad spectrum of images even with underlying changes.
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Larsson, Jimmy. "Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574.

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With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how much the precision and recall of search queries is improved by using a word taxonomy on traditional Text-Based Image Search and Content-Based Image Search. By applying a word taxonomy to different data sources, a strong keyword filter and a keyword extender were implemented and tested. The results show that depending on the implementation, the precision or the recall can be increased. By using a similar approach on real life implementations, it is possible to force images with higher precisions to the front while keeping a high recall value, thus increasing the experienced relevance of image search.
Med den mängd bilder som nu finns tillgänglig på Internet, hur kan vi fortfarande hitta det vi letar efter? Denna uppsats försöker avgöra hur mycket bildprecision och bildåterkallning kan öka med hjälp av appliceringen av en ordtaxonomi på traditionell Text-Based Image Search och Content-Based Image Search. Genom att applicera en ordtaxonomi på olika datakällor kan ett starkt ordfilter samt en modul som förlänger ordlistor skapas och testas. Resultaten pekar på att beroende på implementationen så kan antingen precisionen eller återkallningen förbättras. Genom att använda en liknande metod i ett verkligt scenario är det därför möjligt att flytta bilder med hög precision längre fram i resultatlistan och samtidigt behålla hög återkallning, och därmed öka den upplevda relevansen i bildsök.
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U, Leong Hou. "Web image clustering and retrieval." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445902.

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Manja, Philip. "Image Retrieval within Augmented Reality." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-229922.

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Die vorliegende Arbeit untersucht das Potenzial von Augmented Reality zur Verbesserung von Image Retrieval Prozessen. Herausforderungen in Design und Gebrauchstauglichkeit wurden für beide Forschungsbereiche dargelegt und genutzt, um Designziele für Konzepte zu entwerfen. Eine Taxonomie für Image Retrieval in Augmented Reality wurde basierend auf der Forschungsarbeit entworfen und eingesetzt, um verwandte Arbeiten und generelle Ideen für Interaktionsmöglichkeiten zu strukturieren. Basierend auf der Taxonomie wurden Anwendungsszenarien als weitere Anforderungen für Konzepte formuliert. Mit Hilfe der generellen Ideen und Anforderungen wurden zwei umfassende Konzepte für Image Retrieval in Augmented Reality ausgearbeitet. Eins der Konzepte wurde auf einer Microsoft HoloLens umgesetzt und in einer Nutzerstudie evaluiert. Die Studie zeigt, dass das Konzept grundsätzlich positiv aufgenommen wurde und bietet Erkenntnisse über unterschiedliches Verhalten im Raum und verschiedene Suchstrategien bei der Durchführung von Image Retrieval in der erweiterten Realität
The present work investigates the potential of augmented reality for improving the image retrieval process. Design and usability challenges were identified for both fields of research in order to formulate design goals for the development of concepts. A taxonomy for image retrieval within augmented reality was elaborated based on research work and used to structure related work and basic ideas for interaction. Based on the taxonomy, application scenarios were formulated as further requirements for concepts. Using the basic interaction ideas and the requirements, two comprehensive concepts for image retrieval within augmented reality were elaborated. One of the concepts was implemented using a Microsoft HoloLens and evaluated in a user study. The study showed that the concept was rated generally positive by the users and provided insight in different spatial behavior and search strategies when practicing image retrieval in augmented reality
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Books on the topic "Image retrieval"

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Eakins, J. P. Content-based image retrieval. Manchester: JISC Technology Applications Pogramme, 1999.

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Sundaram, Hari, Milind Naphade, John R. Smith, and Yong Rui, eds. Image and Video Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11788034.

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Lew, Michael S., Nicu Sebe, and John P. Eakins, eds. Image and Video Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45479-9.

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Tyagi, Vipin. Content-Based Image Retrieval. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6759-4.

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Leow, Wee-Kheng, Michael S. Lew, Tat-Seng Chua, Wei-Ying Ma, Lekha Chaisorn, and Erwin M. Bakker, eds. Image and Video Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526346.

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Bakker, Erwin M., Michael S. Lew, Thomas S. Huang, Nicu Sebe, and Xiang Sean Zhou, eds. Image and Video Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7.

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Enser, Peter, Yiannis Kompatsiaris, Noel E. O’Connor, Alan F. Smeaton, and Arnold W. M. Smeulders, eds. Image and Video Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98923.

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Vicario, Enrico, ed. Image Description and Retrieval. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4825-6.

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Urbana-Champaign), Clinic on Library Applications of Data Processing (33rd 1996 University of Illinois at. Digital image access & retrieval. [Urbana-Champaign]: Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, 1997.

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Eakins, J. P. Techniques for image retrieval. London: Library Information Technology Centre, 1998.

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Book chapters on the topic "Image retrieval"

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van der Heijden, Ferdi, and Luuk Spreeuwers. "Image Processing." In Multimedia Retrieval, 125–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_5.

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Grosky, William I. "Image Retrieval." In Encyclopedia of Multimedia, 330–35. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_345.

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Chang, Yih-Chen, and Hsin-Hsi Chen. "Image Sense Classification in Text-Based Image Retrieval." In Information Retrieval Technology, 124–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_11.

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Toselli, Alejandro Héctor, Enrique Vidal, and Francisco Casacuberta. "Interactive Image Retrieval." In Multimodal Interactive Pattern Recognition and Applications, 209–26. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-479-1_11.

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Eakins, John P. "Trademark Image Retrieval." In Principles of Visual Information Retrieval, 319–50. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-3702-3_13.

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Abbadeni, Noureddine. "Perceptual Image Retrieval." In Visual Information and Information Systems, 259–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11590064_23.

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Karlgren, Jussi, and Julio Gonzalo. "Interactive Image Retrieval." In ImageCLEF, 117–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15181-1_7.

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Lestari Paramita, Monica, and Michael Grubinger. "Photographic Image Retrieval." In ImageCLEF, 141–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15181-1_8.

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Ciocca, Gianluigi, Claudio Cusano, Simone Santini, and Raimondo Schettini. "Prosemantic Image Retrieval." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 643–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_72.

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Wang, James Z. "Image Classification by Image Matching." In Integrated Region-Based Image Retrieval, 105–22. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1641-5_7.

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Conference papers on the topic "Image retrieval"

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Penamakuri, Abhirama Subramanyam, Manish Gupta, Mithun Das Gupta, and Anand Mishra. "Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/146.

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We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9% and 11.8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively.
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Zhu, Hao, and Shenghua Gao. "Locality Constrained Deep Supervised Hashing for Image Retrieval." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/499.

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Deep Convolutional Neural Network (DCNN) based deep hashing has shown its success for fast and accurate image retrieval, however directly minimizing the quantization error in deep hashing will change the distribution of DCNN features, and consequently change the similarity between the query and the retrieved images in hashing. In this paper, we propose a novel Locality-Constrained Deep Supervised Hashing. By simultaneously learning discriminative DCNN features and preserving the similarity between image pairs, the hash codes of our scheme preserves the distribution of DCNN features thus favors the accurate image retrieval.The contributions of this paper are two-fold: i) Our analysis shows that minimizing quantization error in deep hashing makes the features less discriminative which is not desirable for image retrieval; ii) We propose a Locality-Constrained Deep Supervised Hashing which preserves the similarity between image pairs in hashing.Extensive experiments on the CIFARA-10 and NUS-WIDE datasets show that our method significantly boosts the accuracy of image retrieval, especially on the CIFAR-10 dataset, the improvement is usually more than 6% in terms of the MAP measurement. Further, our method demonstrates 10 times faster than state-of-the-art methods in the training phase.
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Valem, Lucas Pascotti, and Daniel Carlos Guimarães Pedronette. "Unsupervised Selective Rank Fusion on Content-Based Image Retrieval." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8303.

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Mainly due to the evolution of technologies to store and share images, the growth of image collections have been remarkable for years. Therefore, developing effective methods to index and retrieve such extensive available visual information is indispensable. The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual descriptors and machine learning methods. Despite the relevant advances in the area, mainly driven by deep learning technologies, accurately computing the similarity between images remains a complex task in various scenarios due to the well known semantic gap problem. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the evaluated scenarios.
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Wang, Zhipeng, Hao Wang, Jiexi Yan, Aming Wu, and Cheng Deng. "Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/158.

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Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets.
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"Image retrieval." In 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2014. http://dx.doi.org/10.1109/ipta.2014.7001964.

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Tan, Wei Sheng, Wan Yoke Chin, and Khai Yin Lim. "Content-Based Image Retrieval for Painting Style with Convolutional Neural Network." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1007.

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With the advancement of digital paintings in online collection platform, new image processing algorithms are required to manage digital paintings saved on database. Image retrieval has been one of the most difficult disciplines in digital image processing because it requires scanning a large database for images that are comparable to the query image. It is commonly known that retrieval performance is largely influenced by feature representations and similarity measures. Deep Learning has recently advanced significantly, and deep features based on deep learning have been widely used because it has been demonstrated that the features have great generalisation. In this paper, a convolutional neural network (CNN) is utilised to extract deep and high-level features from the paintings. Next, the features were used for similarity measure between the query image and database images; subsequently, similar images are ranked by the distance between both pair features. Our experiments show that this strategy significantly improves the performance of content-based image retrieval for the style retrieval task of painting. Keywords: Content-based Image Retrieval, Deep Learning, Convolutional Neural Network
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Chao Zhang and Takuya Akashi. "Compressive image retrieval with modified images." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244465.

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Hanbury, Allan, Naeem Bhatti, Mihai Lupu, and Roland Mörzinger. "Patent image retrieval." In the 4th workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2064975.2064979.

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Guo, Li, Jingyu Yang, and Xinghua Sun. "Trademark image retrieval." In Multispectral Image Processing and Pattern Recognition, edited by Jun Tian, Tieniu Tan, and Liangpei Zhang. SPIE, 2001. http://dx.doi.org/10.1117/12.442902.

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Broilo, M., and F. G. B. De Natale. "Evolutionary image retrieval." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413574.

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Reports on the topic "Image retrieval"

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Garris, Michael D. Document image recognition and retrieval:. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6231.

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Conner, M. L. PAMS photo image retrieval prototype alternatives analysis. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/10154323.

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Zhang, Jerry. Large Scale Image Retrieval in Urban Environments with Pixel Accurate Image Tagging. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558987.

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Conner, M. L. PAMS photo image retrieval prototype system requirements specification. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/10154327.

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Liang, Yiqing. Video Retrieval Based on Language and Image Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada364129.

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Conner, M. L. ,. Westinghouse Hanford. PAMS Photo Image Retrieval Prototype System Design Description. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/662025.

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Lee, Jung-Eun, Rong Jin, and Anil K. Jain. Ranked-Based Distance Metric Learning: An Application to Image Retrieval. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada500953.

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Kwong, M. K., and B. Lin. Large-scale indexing and retrieval system for local image features. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/505383.

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Conser, Erik. Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5767.

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Das, M., B. A. Draper, W. J. Lim, R. Manmatha, and E. M. Riseman. A Fast, Background-Independent Retrieval Strategy for Color Image Databases. Fort Belvoir, VA: Defense Technical Information Center, November 1996. http://dx.doi.org/10.21236/ada477660.

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