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Relevant bibliographies by topics / Fine-grained image analysi

Academic literature on the topic 'Fine-grained image analysi'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Fine-grained image analysi"

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Lv, Zhihan, Liang Qiao, Amit Kumar Singh, and Qingjun Wang. "Fine-Grained Visual Computing Based on Deep Learning." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (April 20, 2021): 1–19. http://dx.doi.org/10.1145/3418215.

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With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build a multi-level fine-grained image feature categorization model. Finally, the TensorFlow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.

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Zhou, Dehui. "Image Recognition of Pledges of Capital Stock in Small- and Medium-Sized Enterprises Based on Partial Differential Equations." Advances in Mathematical Physics 2021 (November 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/6548344.

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Image recognition is one of the core research directions in the field of computer vision research, which can be divided into general image recognition and fine-grained image recognition. General image recognition refers to the recognition of different types of objects; fine-grained image recognition refers to the recognition of different subclasses in the same broad class of objects, such as SME financing inventory pledge image recognition. In this paper, we propose a partial differential equation-based image recognition method for SME financing inventory pledges and conduct detailed analysis and experiments. Compared with general images, partial differential equation-based SME financing inventory pledges image recognition is difficult to recognize due to data characteristics such as small differences in features between classes, large differences in features within classes, and a small percentage of targets in the image. To address the problem that existing methods ignore the role of shallow features on fine-grained image recognition, this paper proposes a fine-grained image recognition method based on partial differential equations. By analyzing the important role of shallow features for fine-grained image recognition, a feature fusion method with adaptive weights is proposed. Using this method to fuse shallow and high-level semantic features for recognition, the role of shallow features in fine-grained image recognition is fully exploited. In addition, the proposed method does not change the order of magnitude of the model parameters and is highly transferable. The relevant experimental results verify the effectiveness of the proposed method.

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Sun, Yingxue, and Junbo Gao. "Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism." Electronics Science Technology and Application 7, no. 4 (January 21, 2021): 123. http://dx.doi.org/10.18686/esta.v7i4.166.

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<p>In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis.</p>

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Wang, Xiaoqin, Rushi Lan, Huadeng Wang, Zhenbing Liu, and Xiaonan Luo. "Fine-grained correlation analysis for medical image retrieval." Computers & Electrical Engineering 90 (March 2021): 106992. http://dx.doi.org/10.1016/j.compeleceng.2021.106992.

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Yan, Yichao, Bingbing Ni, Huawei Wei, and Xiaokang Yang. "Fine-grained image analysis via progressive feature learning." Neurocomputing 396 (July 2020): 254–65. http://dx.doi.org/10.1016/j.neucom.2018.07.100.

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Guo, Jinyi, Wei Ren, Yi Ren, and Tianqin Zhu. "A Watermark-Based in-Situ Access Control Model for Image Big Data." Future Internet 10, no. 8 (July 29, 2018): 69. http://dx.doi.org/10.3390/fi10080069.

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When large images are used for big data analysis, they impose new challenges in protecting image privacy. For example, a geographic image may consist of several sensitive areas or layers. When it is uploaded into servers, the image will be accessed by diverse subjects. Traditional access control methods regulate access privileges to a single image, and their access control strategies are stored in servers, which imposes two shortcomings: (1) fine-grained access control is not guaranteed for areas/layers in a single image that need to maintain secret for different roles; and (2) access control policies that are stored in servers suffers from multiple attacks (e.g., transferring attacks). In this paper, we propose a novel watermark-based access control model in which access control policies are associated with objects being accessed (called an in-situ model). The proposed model integrates access control policies as watermarks within images, without relying on the availability of servers or connecting networks. The access control for images is still maintained even though images are redistributed again to further subjects. Therefore, access control policies can be delivered together with the big data of images. Moreover, we propose a hierarchical key-role-area model for fine-grained encryption, especially for large size images such as geographic maps. The extensive analysis justifies the security and performance of the proposed model

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Kothari, Kushal, Ajay Arjunwadkar, Hitesh Bhalerao, and Savita Lade. "Fine-Grained Identification of Clothing Apparels." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3168–71. http://dx.doi.org/10.22214/ijraset.2022.42022.

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Abstract: The rapid use of smartphones and tablet computers, search is now not just limited to text,but moved to other modalities such as voice and image. Extracting and matching this attributes still remains a daunting task due to high deformability and variability of clothing items. Visual analysis of clothings is a topic that has received attention due to tremendous growth of e-commerce fashion stores. Analyzing fashion attributes is also crucial in the fashion design process. This paper addresses the solution of recognition of clothes and fashion related attributes related to it using better image segmentation RCNN based algorithms. Keywords: Computer Vision, Fine grained identification, Clothing apparel detection, Convolutional Neural Network, Mask RCNN, Detectron2

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Ma, Kai, Ming-Jun Nie, Sen Lin, Jianlei Kong, Cheng-Cai Yang, and Jinhao Liu. "Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes." Algorithms 14, no. 10 (September 30, 2021): 290. http://dx.doi.org/10.3390/a14100290.

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Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

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Liu, Haipeng, Jiangtao Wang, Yayuan Geng, Kunwei Li, Han Wu, Jian Chen, Xiangfei Chai, Shaolin Li, and Dingchang Zheng. "Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning." International Journal of Environmental Research and Public Health 19, no. 17 (August 26, 2022): 10665. http://dx.doi.org/10.3390/ijerph191710665.

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Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

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Hirota, Katsuya, Tomoko Ariga, Masahiro Hino, Go Ichikawa, Shinsuke Kawasaki, Masaaki Kitaguchi, Kenji Mishima, Naoto Muto, Naotaka Naganawa, and Hirohiko M. Shimizu. "Neutron Imaging Using a Fine-Grained Nuclear Emulsion." Journal of Imaging 7, no. 1 (January 5, 2021): 4. http://dx.doi.org/10.3390/jimaging7010004.

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A neutron detector using a fine-grained nuclear emulsion has a sub-micron spatial resolution and thus has potential to be applied as high-resolution neutron imaging. In this paper, we present two approaches to applying the emulsion detectors for neutron imaging. One is using a track analysis to derive the reaction points for high resolution. From an image obtained with a 9 μm pitch Gd grating with cold neutrons, periodic peak with a standard deviation of 1.3 μm was observed. The other is an approach without a track analysis for high-density irradiation. An internal structure of a crystal oscillator chip, with a scale of approximately 30 μm, was able to be observed after an image analysis.

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Dissertations / Theses on the topic "Fine-grained image analysi"

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FAMOURI, SINA. "Machine learning methods for the analysis and interpretation of images and other multi-dimensional data." Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2972835.

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Book chapters on the topic "Fine-grained image analysi"

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Shustrov, Dmitrii, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, and Heikki Haario. "Fine-Grained Wood Species Identification Using Convolutional Neural Networks." In Image Analysis, 67–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7_6.

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Trenta, Francesca, Alessandro Ortis, and Sebastiano Battiato. "Fine-Grained Image Classification for Pollen Grain Microscope Images." In Computer Analysis of Images and Patterns, 341–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89128-2_33.

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Murabito, Francesca, Simone Palazzo, Concetto Spampinato, and Daniela Giordano. "Generating Knowledge-Enriched Image Annotations for Fine-Grained Visual Classification." In Image Analysis and Processing - ICIAP 2017, 332–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_30.

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Fu, Jianlong, and Tao Mei. "Image Tagging with Deep Learning: Fine-Grained Visual Analysis." In Big Data Analytics for Large-Scale Multimedia Search, 267–87. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119376996.ch10.

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Leutgeb, Lorenz, Georg Moser, and Florian Zuleger. "Automated Expected Amortised Cost Analysis of Probabilistic Data Structures." In Computer Aided Verification, 70–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_4.

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AbstractIn this paper, we present the first fully-automated expected amortised cost analysis of self-adjusting data structures, that is, of randomised splay trees, randomised splay heaps and randomised meldable heaps, which so far have only (semi-)manually been analysed in the literature. Our analysis is stated as a type-and-effect system for a first-order functional programming language with support for sampling over discrete distributions, non-deterministic choice and a ticking operator. The latter allows for the specification of fine-grained cost models. We state two soundness theorems based on two different—but strongly related—typing rules of ticking, which account differently for the cost of non-terminating computations. Finally we provide a prototype implementation able to fully automatically analyse the aforementioned case studies."Image missing"

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Matuszek, Dominika B., Łukasz A. Biłos, and Jolanta B. Królczyk. "Image Analysis in the Assessment of Homogeneity of Fine-Grained Mixtures." In Lecture Notes in Civil Engineering, 179–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13090-8_19.

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La Grassa, Riccardo, Ignazio Gallo, and Nicola Landro. "EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task." In Computer Analysis of Images and Patterns, 393–402. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89128-2_38.

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Ridzuan, Muhammad, Ameera Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, and Mohammad Yaqub. "Self-supervision and Multi-task Learning: Challenges in Fine-Grained COVID-19 Multi-class Classification from Chest X-rays." In Medical Image Understanding and Analysis, 234–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_18.

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"Benthic Habitats and the Effects of Fishing." In Benthic Habitats and the Effects of Fishing, edited by J. M. PRESTON, A. C. CHRISTNEY, W. T. COLLINS, and B. D. BORNHOLD. American Fisheries Society, 2005. http://dx.doi.org/10.47886/9781888569605.ch31.

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Seabed images, from multibeam systems or sidescans, convey a lot of information about seabed type. Large-scale rocky relief often gives dramatic images, and morphology such as sand waves can be very evident. Fine-grained sediments affect images in less obvious ways. Statistical processing of the backscatter amplitudes generate features adequate for seabed classification that agree with both largescale interpretation and fine-grained details. Before calculating features, it is essential to precondition the image by compensating for artifacts due to range and grazing angle. Useful features include ratios of integrals of the power spectrum over various frequency bands, descriptors of grey-level co-occurrence matrices and histograms, means and higher order moments, and fractal dimension. Generating many features and then using multivariate statistical techniques to select the linear combinations that capture most of the variance in the dataset improves the quality and usefulness of the resulting classifications by adapting the classification to each set of images. To complete the classification process, records are assigned to classes by the same clustering process used in the existing Quester Tangent classification products. Maps of these acoustic classes show regions of distinct acoustic character, thus of distinct sediment type in some sense. To make all this useful for benthic studies, one must understand how this acoustic diversity correlates with the distribution of species of interest. Various spatial analysis techniques are available to accomplish this, and several examples of the integration of acoustic and benthic information will be presented.

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Conference papers on the topic "Fine-grained image analysi"

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Ha, Mai Lan, and Volker Blanz. "Neural Discriminant Analysis For Fine-Grained Classification." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190954.

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Bender, Sidney, Monica Haurilet, Alina Roitberg, and Rainer Stiefelhagen. "Learning Fine-Grained Image Representations for Mathematical Expression Recognition." In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). IEEE, 2019. http://dx.doi.org/10.1109/icdarw.2019.00015.

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Zhu, Yaohui, Chenlong Liu, and Shuqiang Jiang. "Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/152.

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The goal of few-shot image recognition is to distinguish different categories with only one or a few training samples. Previous works of few-shot learning mainly work on general object images. And current solutions usually learn a global image representation from training tasks to adapt novel tasks. However, fine-gained categories are distinguished by subtle and local parts, which could not be captured by global representations effectively. This may hinder existing few-shot learning approaches from dealing with fine-gained categories well. In this work, we propose a multi-attention meta-learning (MattML) method for few-shot fine-grained image recognition (FSFGIR). Instead of using only base learner for general feature learning, the proposed meta-learning method uses attention mechanisms of the base learner and task learner to capture discriminative parts of images. The base learner is equipped with two convolutional block attention modules (CBAM) and a classifier. The two CBAM can learn diverse and informative parts. And the initial weights of classifier are attended by the task learner, which gives the classifier a task-related sensitive initialization. For adaptation, the gradient-based meta-learning approach is employed by updating the parameters of two CBAM and the attended classifier, which facilitates the updated base learner to adaptively focus on discriminative parts. We experimentally analyze the different components of our method, and experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our method.

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"AUTOMATIC FINE-GRAINED LABELING OF BRAIN MR IMAGES - A CRF Approach." In Special Session on Medical Image Analysis and Description for Diagnosis Systems. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003890005210526.

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Seo, Yian, and Kyung-shik Shin. "Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network." In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbda.2018.8367713.

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SU, Zhibin, Jing Peng, Ren Hui, and Yunfang Zhang. "Fine-grained Sentiment Semantic Analysis and Matching of Music and Image." In 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ). IEEE, 2022. http://dx.doi.org/10.1109/iaeac54830.2022.9929967.

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Zhang, Xiaofan, Hai Su, Lin Yang, and Shaoting Zhang. "Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7299174.

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Lin, Yifeng. "Research progress and frontier analysis of fine-grained image classification based on CiteSpace." In 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), edited by Deqiang Cheng and Omer Deperlioglu. SPIE, 2022. http://dx.doi.org/10.1117/12.2643842.

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Li, Yuwei, Minye Wu, Yuyao Zhang, Lan Xu, and Jingyi Yu. "PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging." 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.2021/113.

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Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.

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Xu, Jianrong, Boyu Diao, Bifeng Cui, Chao Li, Yongjun Xu, and Hailong Hong. "Analysis of the Influence Degree of Network Pruning on Fine-grained Image Processing Tasks." In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). IEEE, 2021. http://dx.doi.org/10.1109/icsip52628.2021.9688612.

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