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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Suma, K. Sai Aditya, G. Mukherjee, Snehasis Indian Inst Informat Technol Sricity Chittoor Andhra Pradesh India

ISBN: (纸本)9781450366151

In this paper we present a novel methodology for recognizing human activity in Egocentric video based on the Bag of Visual Features. the proposed technique is based on the assumption that, only a portion of the whole video can be sufficient to identify an activity. Rather, we argue that, for activity recognition in egocentric videos, the proposed approach performs better than any deep learning based method. Because, in egocentric videos, often the person wiring the sensor, becomes static for long time, or moves his head frequently. In both the cases, it becomes difficult to learn the spatio-temporal pattern of the video during action. the proposed approach divides the video into smaller video segments called Video Units. Spatio-temporal features extracted from the units, are clustered to construct the dictionary of Action Units (AU). the AUs are ranked based upon their score of likeliness. the scores are obtained by constructing a weighted graph with the AUs as vertices and edge weights calculated based on the frequencies of occurrences of the AUs during the activity. the less significant AUs are pruned out from the dictionary, and the revised dictionary of key AUs are used for activity *** test our approach on benchmark egocentric dataset and achieve a good accuracy.

关键词： Deep learning

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Moving Average Recurrent Neural Network Model for Video-based Person Re-Identification 11

Moving Average Recurrent Neural Network Model for Video-base...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Fasil, Muhammed C. Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9781450366151

Person re-identification has great applications in video surveillance. It can be viewed as recognizing the same person across non-overlapping cameras. Video-based person re-identification methods are gaining increased attention due to the better discriminative nature of spatio-temporal feature representations. Current video-based methods make use of RNN to extract temporal information. In this paper, we propose a novel Moving Average Recurrent Neural Network (MA-RNN) model that can build a strong feature representation by taking both previous and present inputs at each time stamp. Specifically, here the recurrent layer produces a better sequential information by looking back directly in to the past values where as general RNNs has only an indirect dependence on the previous values in the form of hidden-state information. the proposed model is tested on two publicly available datasets: iLIDS-VID and PRID-2011 and it performed better in comparison with the state-of-the-art methods with a significant margin. We also analyze the effect of the depth of previous input dependence of the MA-RNN model on the matching accuracy.

关键词： RNN MA-RNN person re-identification

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Subjective performance of texture based algorithm for face verification: the role of databases

Subjective performance of texture based algorithm for face v...

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6th indian conference on computer vision, graphics and image processing, ICVGIP 2008

作者： Raghavendra, R. Rao, Ashok Kumar.G, Hemantha Department of studies in Computer Science University of Mysore Mysore-570 006 India Dept of Computer Science J.S.S. College of Arts Commerce and Science Mysore-570 025 India

ISBN: (纸本)9780769534763

this paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. the proposed method consists of three parts. the first part is a Log-Gabor filtering on facial image. the second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. the third part is transforming the set of features using subspace methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA). thus, in this paper two methods namely Log-Gabor Mixture Model (LGMM) based on PCA and Log-Gabor Mixture Model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. the nature of type & size of databases chosen on the performance of these algorithms are also studied. the experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms. © 2008 IEEE.

关键词： Principal component analysis

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DP-GAN: Dual Pathway Generative Adversarial Network for Face Recognition in Degraded Scenarios 11

DP-GAN: Dual Pathway Generative Adversarial Network for Face...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhattacharjee, Avishek Banerjee, Samik Das, Sukhendu Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. they however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. these probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. these images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. these images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. the proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.

关键词： Face recognition

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Advanced Concepts for Intelligent vision Systems 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Jacques Blanc-Talon Andrzej Kasinski Wilfried Philips Dan Popescu Paul Scheunders

ISBN: (数字)9783319028958

ISBN: (纸本)9783319028941

this book constitutes the thoroughly refereed proceedings of the 15th International conference on Advanced Concepts for Intelligent vision Systems, ACIVS 2013, held in Poznań, Poland, in October 2013. the 63 revised full papers were carefully selected from 111 submissions. the topics covered are aquisition, pre-processing and coding, biometry, classification and recognition, depth, 3D and tracking, efficient implementation and frameworks, low level image analysis, segmentation and video analysis.

关键词： image processing and computer vision Artificial Intelligence Pattern Recognition computer graphics computer Communication Networks Algorithm Analysis and Problem Complexity

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computer vision – ECCV 2018 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Ferrari Cristian Sminchisescu Martial Hebert Yair Weiss

ISBN: (数字)9783030012496

ISBN: (纸本)9783030012489

the sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European conference on computer vision, ECCV 2018, held in Munich, Germany, in September 2018.;the 776 revised papers presented were carefully reviewed and selected from 2439 submissions. the papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

关键词： image processing and computer vision computer graphics Artificial Intelligence computer Communication Networks

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image retrieval based on indexing and relevance feedback

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PATTERN RECOGNITION LETTERS 2007年第3期28卷 357-366页

作者： Saha, Sanjoy K. Das, Amit K. Chanda, Bhabatosh Jadavpur Univ CSE Dept Kolkata 700032 W Bengal India BE Coll DU CST Dept Howrah 711103 India Indian Stat Inst ECS Unit Kolkata 700035 W Bengal India

In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. the indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann-Whitney test. the test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. then emphasis of the discriminating features are updated to improve the retrieval performance. the relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). the experiment justifies the effectiveness of the proposed methodologies. Finally, the indexing scheme and relevance feedback mechanism are combined to build up the search engine. (c) 2006 Elsevier B.V. All rights reserved.

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Salient Object Detection based on Bayesian Surprise of Restricted Boltzmann Machine 11

Salient Object Detection based on Bayesian Surprise of Restr...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Roy, Rahul Ghosh, Susmita Ghosh, Ashish Indian Stat Inst Machine Intelligence Unit Kolkata W Bengal India Jadavpur Univ Dept Comp Sci & Technol Kolkata W Bengal India

ISBN: (纸本)9781450366151

this article presents an algorithm for salient object detection by leveraging the Bayesian surprise of the Restricted Boltzmann Machine (RBM). Here an RBMis trained on patches sampled randomly from the input image. Due to this random sampling, the RBM is likely to get more exposed to background patches than that of the object. thus, the trained RBM will minimize the free energy of its hidden states with respect to the background patches as opposed to the object. this, according to the free energy principle, implies minimizing Bayesian surprise which is a measure for saliency based on Kullback Leibler divergence between the input and reconstructed patch distribution. Hence, when the trained RBM is exposed to patches from the object region, it would have high divergence and in turn a high Bayesian surprise. thus such pixels with high Bayesian surprise could be considered as salient pixels. For each pixel, a neighborhood (with the same size of training patch) is considered and is fed to the trained RBM to obtain the reconstructed patch. thereafter, the Kullback Leibler divergence between the input and reconstructed neighborhood of each pixel is computed to measure the Bayesian surprise and is stored in the corresponding position in a matrix to form the saliency map. Experiments are carried out on three datasets namelyMSRA-10K, ECSSD and DUTS. the results obtained depict promising performance by the proposed approach.

关键词： Object Detection Restricted Boltzmann Machine Bayesian Surprise Free Energy Principle

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computer vision – ECCV 2018 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Ferrari Cristian Sminchisescu Martial Hebert Yair Weiss

ISBN: (数字)9783030012281

ISBN: (纸本)9783030012274

关键词： image processing and computer vision computer graphics Artificial Intelligence Systems and Data Security

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image Analysis and processing – ICIAP 2009 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Pasquale Foggia Carlo Sansone Mario Vento

ISBN: (数字)9783642041464

ISBN: (纸本)9783642041457

this book constitutes the refereed proceedings of the 15th International conference on image Analysis and processing, ICIAP 2009, held in Vietri sul Mare, Italy, in September 2009. the 107 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 168 submissions. the papers are organized in topical sections on computer graphics and image processing, low and middle level processing, 2D and 3D segmentation, feature extraction and image analysis, object detection and recognition, video analysis and processing, pattern analysis and classification, learning, graphs and trees, applications, shape analysis, face analysis, medical imaging, and image analysis and pattern recognition.

关键词： image processing and computer vision Pattern Recognition Artificial Intelligence computer graphics Algorithm Analysis and Problem Complexity

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