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检索条件"机构=Computer Vision and Pattern Recognition"

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Attention-Driven Dynamic Graph Convolutional Network for Multi-label Image recognition 16th

Attention-Driven Dynamic Graph Convolutional Network for Mul...

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16th European Conference on computer vision, ECCV 2020

作者： Ye, Jin He, Junjun Peng, Xiaojiang Wu, Wenhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030585884

Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition. However, constructing a graph by counting the label co-occurrence possibilities of the training data may degrade model generalizability, especially when there exist occasional co-occurrence objects in test images. Our goal is to eliminate such bias and enhance the robustness of the learnt features. To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic Graph Convolutional Network (D-GCN) to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM). Extensive experiments on public multi-label benchmarks demonstrate the effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current state-of-the-art methods with a clear margin. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework

Diffusion-snakes: combining statistical shape knowledge and ...

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IEEE Workshop on Variational and Level Set Methods in computer vision

作者： D. Cremers C. Schnorr J. Weickert Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim Germany

ISBN: (纸本)076951278X

We present a modification of the Mumford-Shah functional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level-set implementation of geodesic active contours.

关键词： Image segmentation Level set Active contours Spline Shape control Active shape model Equations Statistical learning computer vision computer graphics

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A new method for sclera vessel recognition using OLBP

引用

2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Das, Abhijit Pal, Umapada Ferrer Ballester, Miguel A. Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9783319029603

This paper proposes a new sclera vessel recognition technique. The vessel patterns of sclera are unique for each individual and this can be utilized to identify a person uniquely. In this research we have used a time adaptive active contour-based region growing technique for sclera segmentation. Prior to that, we have made some tonal and illumination correction to get a clearer sclera area without the distributing vessel structure. This is because the presence of complex vessel structures occasionally affects the region-growing process. The sclera vessels are not prominent in the images, so in order to make them clearly visible, a local image enhancement process using a Haar high pass filter is incorporated. To get the total orientation of the vessels, we have used Orientated Local Binary pattern (OLBP). The OLBP images of each class are used for template matching for classification by calculating the minimum Hamming Distance. We have used the UBIRIS version 1 dataset for the experimentation of our research. The proposed approach has achieved high recognition accuracy employing the above-mentioned dataset. © Springer International Publishing 2013.

关键词： Local binary pattern

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Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

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15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

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A New Lightweight Attention-Based Model for Emotion recognition on Distorted Social Media Face Images 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Gornale, Shivanand S. Liu, Cheng-Lin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Department of Computer Science Rani Channamma University Belagavi India Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

The recognition of human emotions remains a challenging task for social media images. This is due to distortions created by different social media conflict with the minute changes in facial expression. This study presents a new model called the Global Spectral-Spatial Attention Network (GSSAN), which leverages both local and global information simultaneously. The proposed model comprises a shallow Convolutional Neural Network (CNN) with an MBResNext block, which integrates the features extracted from MobileNet, ResNet, and DenseNet for extracting local features. In addition, to strengthen the discriminating power of the features, GSSAN incorporates Fourier features, which provide essential cues for minute changes in the face images. To test the proposed model for emotion recognition using social media images, we conduct experiments on two widely-used datasets: FER-2013 and AffectNet. The same benchmark datasets are uploaded and downloaded to create a distorted social media image dataset to test the proposed model. Experiments on distorted social media images dataset show that the model surpasses the accuracy of SOTA models by 0.69% for FER-2013 and 0.51% for AffectNet social media datasets. The same inference can be drawn from the experiments on standard datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Face recognition

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SVM and nn based offline signature verification

SVM and nn based offline signature verification

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作者： Pal, Srikanta Alaei, Alireza Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Gold Coast Griffith University Australia Polytech'Tours Université François-Rabelais de Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English based signature verification. To fill this gap, a threshold-based scheme for the verification of off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/end point and directional chain code are employed for feature extraction. The thresholds are computed based on the similarity measures obtained employing the nearest neighbor classifier. The SVM classifier has also been considered for mainly comparative experimental result generation. Furthermore, a Bangla signature database, which consists of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries, has been created and is employed for experimentation. An average error rate (AER) of 12.33% was obtained as the best verification result using directional chain code features in this research work. As a comparative study, a different dataset (GPDS-160) has also been considered. © 2013 Imperial College Press.

关键词： Codes (symbols)

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Sclera vessel pattern synthesis based on a non-parametric texture synthesis technique

Sclera vessel pattern synthesis based on a non-parametric te...

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International Conference on computer vision and Image Processing, CVIP 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Blumenstein, Michael Ferrer, Miguel A. Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9789811021060

This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers. © Springer Science+Business Media Singapore 2017.

关键词： Biometrics pattern Sclera Synthesis Texture

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Image co-clustering with multi-modality features and user feedbacks 09

Image co-clustering with multi-modality features and user fe...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Chen, Yanhua Dong, Ming Wan, Wanggen Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States School of Communication and Info. Eng. Shanghai University Shanghai 200072 China

ISBN: (纸本)9781605586083

In Content-based Image Retrieval (CBIR) research, advanced technology that fuses the heterogeneous information into image clustering has drawn extensive attention recently. However, using multiple features for co-clustering images without any user feedbacks is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for image co-clustering. Our method computes new relational matrices by incorporating user provided feedbacks into images through simultaneous distance metric learning and feature selection for different low-level visual features. Using an iterative algorithm, we perform tri-factorizations of the new matrices to infer image clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on image data sets, we demonstrate that SS-NMF provides an effective and efficient solution for image co-clustering. Copyright 2009 ACM.

关键词： Matrix algebra

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Design of surrogate models in civil engineering by neural networks 9

Design of surrogate models in civil engineering by neural ne...

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9th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Drahy, Vojtech Marik, Radek Kalviainen, Heikki Czech Technical University in Prague Department of Computer Science Prague Czech Republic Czech Technical University in Prague Department of Telecommunication Engineering Prague Czech Republic Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta Finland

ISBN: (纸本)9798350342482

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. © 2024 IEEE.

关键词： Neural network models

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