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Locality-preserving K-SVD Based Joint Dictionary and Classifier Learning for Object Recognition

Yuan-Shan Lee, Chien-Yao Wang, Seksan Mathulaprangsan, Jia-Hao Zhao, Jia-Ching Wang
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
For testing, additional information about the locality of query samples is obtained by treating the locality-preserving matrix as a feature.  ...  This paper concerns the development of locality-preserving methods for object recognition.  ...  Generally, the process of object recognition can be divided into two main steps, which are feature extraction and classification. Diverse methods exist for extracting the features from image files.  ... 
doi:10.1145/2964284.2967267 dblp:conf/mm/LeeWMZW16 fatcat:rx7htjr4afgcbaqrixvln2ecue

Multiple-view object recognition in band-limited distributed camera networks

Allen Y. Yang, Subhransu Maji, C. Mario Christoudias, Trevor Darrell, Jitendra Malik, S. Shankar Sastry
2009 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)  
Due to the limited bandwidth between the cameras and the computer, the method utilizes the available computational power on the smart sensors to locally extract and compress SIFT-type image features to  ...  Such joint sparse patterns can be explicitly exploited to accurately encode the distributed signal via random projection, which is unsupervised and independent to the sensor modality.  ...  The authors thank Kirak Hong and Posu Yan of the University of California, Berkeley, for the implementation of the SURF function on the Berkeley CITRIC camera platform.  ... 
doi:10.1109/icdsc.2009.5289410 dblp:conf/icdsc/YangMCDMS09 fatcat:bix4xkfofjfd5ccd6pjdk4fhka

Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery [article]

Zhao Zhang, Lei Wang, Sheng Li, Yang Wang, Zheng Zhang, Zhengjun Zha, Meng Wang
2019 arXiv   pre-print
In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features  ...  Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse  ...  the projection for feature extraction.  ... 
arXiv:1908.07860v2 fatcat:xbv2ybchqfe33jukjq3c7usmtu

Target recognition in SAR image based on robust locality discriminant projection

Meiting Yu, Siqian Zhang, Ganggang Dong, Lingjun Zhao, Gangyao Kuang
2018 IET radar, sonar & navigation  
In this study, a feature extraction method based on robust locality discriminant projection (RLDP) is presented for SAR target recognition.  ...  Extracting valuable and discriminative features is one of the crucial issues for target recognition in synthetic aperture radar (SAR) images.  ...  Motivated by the above works, this paper proposes a new feature extraction method called robust locality discriminant projection (RLDP) for target recognition in SAR images.  ... 
doi:10.1049/iet-rsn.2018.5132 fatcat:4lxfgo3m2jdppfp4zwj4weozxq

Joint sparsity and collaboration preserving projections for rotating electrical machinery fault diagnosis

Yue Ma, Xiaohua Wu
2020 IEEE Access  
To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed  ...  A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs.  ...  [36] proposed a new algorithm named sparse locality preserving discriminative projections (SLPDP) for face recognition, which took local and global information into consideration as well as sparsity  ... 
doi:10.1109/access.2020.3029194 fatcat:imdfmky3njaufkzeoatwuci67m

Synthetic aperture radar target recognition using weighted multi-task kernel sparse representation

Chen Ning, Wenbo Liu, Gong Zhang, Xin Wang
2019 IEEE Access  
Then, the proposed method provides a unified framework, named multi-task kernel sparse representation, for SAR target classification.  ...  As an extension of traditional sparse representation (SR), kernel SR has received great interest recently in the areas of computer vision and pattern recognition.  ...  ACKNOWLEDGMENT The authors thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1109/access.2019.2959228 fatcat:vtibvr2fg5e3jmqyajuijpmuta

Locality-constrained group sparse representation for robust face recognition

Yu-Wei Chao, Yi-Ren Yeh, Yu-Wen Chen, Yuh-Jye Lee, Yu-Chiang Frank Wang
2011 2011 18th IEEE International Conference on Image Processing  
We advance both group sparsity and data locality and formulate a unified optimization framework, which produces a locality and group sensitive sparse representation (LGSR) for improved recognition.  ...  This paper presents a novel sparse representation for robust face recognition.  ...  Acknowledgements This work is supported in part by National Science Council of Taiwan via NSC 99-2221-E-001-020 and NSC 100-2631-H-001-013.  ... 
doi:10.1109/icip.2011.6116666 dblp:conf/icip/ChaoYCLW11 fatcat:s54h6c364vdprdwgvvzijvcwke

Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification

Dongyu Yang, Xinchen Ye, Baolong Guo, Wei Wang
2021 Complexity  
This paper presents an in-depth study and analysis of Chinese painting image classification by a multitask joint sparse representation algorithm for texture feature extraction of Chinese painting images  ...  and proposes a method to extract texture features directly for the original images.  ...  Acknowledgments is research project was supported by Science Foundation of Beijing Language and Culture University (supported by the Fundamental Research Funds for the Central Universities) (Approval number  ... 
doi:10.1155/2021/5546338 fatcat:hsft3uwicrbdpcci7qkbhbqbom

Handwriting representation and recognition through a sparse projection and low-rank recovery framework

Zhao Zhang, Cheng-Lin Liu, Mingbo Zhao
2013 The 2013 International Joint Conference on Neural Networks (IJCNN)  
SPLRR calculates a similarity preserving sparse projection for salient feature extraction and processing new data for classification in addition to delivering a low-rank principal component and identifying  ...  This paper proposes a Robust Principal Component Analysis (RPCA) based framework called Sparse Projection and Low-Rank Recovery (SPLRR) for representing and recognizing handwritings.  ...  An important aspect of our SPLRR is to seek a similarity preserving sparse projection to extract the saliency features from training samples, so Sparse Representation (SR) [17] , [19] is a related criterion  ... 
doi:10.1109/ijcnn.2013.6707050 dblp:conf/ijcnn/ZhangLZ13 fatcat:mtlkrgfrnzdsxefbv5fd6thrne

Sparse preserving feature weights learning

Guangsheng Xia, Hui Yan, Jian Yang
2016 Neurocomputing  
In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory, sparse  ...  Abstract In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory  ...  Strictly speaking these algorithms belong to sparse feature extraction, not feature selection. 50 In this paper, we introduce sparse representation [18] , [19] based measurement criterion and joint  ... 
doi:10.1016/j.neucom.2015.12.020 fatcat:evugk4mxpjhmrjw3q4rxwsyi5u

Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

Wang Wei, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle, Li Ji
2019 Computational Intelligence and Neuroscience  
Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary.  ...  An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper.  ...  CNN can automatically extract complex global and local features from images [8] . erefore, D-AJSR introduces the deep features extracted by CNN into sparse representation to enhance the recognition ability  ... 
doi:10.1155/2019/8258275 pmid:31871442 pmcid:PMC6906836 fatcat:lgqw6fcjanbvjceig26zd4bu6u

Action Recognition Based on Sub-action Motion History Image and Static History Image

Shichao Zhang, Enqing Chen, Chen Qi, Chengwu Liang, M. Kavakli, M.J.E. Salami, A. Amini, M.A.B.M. Basri, A.B. Masli, S.C.H. Li, M. Pal
2016 MATEC Web of Conferences  
The Local Binary Patterns (LBP) descriptor is then computed from the SMHI and SHI for the representation of an action. We evaluate the proposed framework on MSR Action3D dataset.  ...  In this paper, we propose a robust and effective framework to largely improve the performance of human action recognition using depth maps.  ...  Feature Descriptor and Recognition In order to make our feature has a better discriminative power; we further extract the LBP feature [11] for each template.  ... 
doi:10.1051/matecconf/20165602006 fatcat:i5u67nwilbaltawmzogdhtgvvm

A multiview representation framework for micro-expression recognition

Tianhuan Huang, Lei Chen, Yuncong Feng, Xianye Ben, Ruixue Xiao, Tianle Xue
2019 IEEE Access  
Firstly, the features of the two domains are projected into a common space and the dictionaries of each domain are studied respectively.  ...  INDEX TERMS Multiview representation, transfer learning, sparse dictionary learning, micro-expression recognition.  ...  Feature extraction methods include local feature extraction and global feature extraction. Pfister et al. [6] firstly extended the features of the Local Binary Pattern (LBP).  ... 
doi:10.1109/access.2019.2932784 fatcat:jawtrskacfcjtk4mjekcbfkwae

Distributed Sensor Perception via Sparse Representation

Allen Y Yang, Michael Gastpar, Ruzena Bajcsy, S Shankar Sastry
2010 Proceedings of the IEEE  
First, we discuss the question of which projections of the data should be acquired, and how many of them.  ...  Then, we discuss how to take advantage of possible joint sparsity of the signals acquired by multiple sensors, and show how this can further improve the inference of the events from the sensor network.  ...  projection matrix that only extracts the lowdimensional features from the first L nodes.  ... 
doi:10.1109/jproc.2010.2040797 fatcat:yyyuc55ghfhcndcpdp5ilf4jy4

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning

Yawen Huang, Ling Shao, Alejandro F. Frangi
2018 IEEE Transactions on Medical Imaging  
extracted features to preserve the local geometrical structure in each modality.  ...  Inspired by such a strategy, we capture and preserve the local geometric structure of each modality using the projected feature space.  ... 
doi:10.1109/tmi.2017.2781192 pmid:29533896 fatcat:fz5i3lyms5hsnnnfhpi2mjkadm
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