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Sparse approximated nearest points for image set classification

Yiqun Hu, Ajmal S. Mian, Robyn Owens
2011 CVPR 2011  
the nearest points as well as their sparse approximations.  ...  We introduce a novel between-set distance called Sparse Approximated Nearest Point (SANP) distance.  ...  Cevikalp for sharing the source code of AHISD/CHISD and providing the LBP features for the Mobo dataset.  ... 
doi:10.1109/cvpr.2011.5995500 dblp:conf/cvpr/HuMO11 fatcat:dot53wdgnjecfm4oadagki2iqq

Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces

Shaokang Chen, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).  ...  Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets.  ...  Thus the image set classification problem can be transferred to a point classification problem on Grassmann manifolds.  ... 
doi:10.1109/cvpr.2013.65 dblp:conf/cvpr/ChenSHL13 fatcat:tkjau746sjfxze3g2tpuasukwq

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Yiqun Hu, Ajmal S. Mian, Robyn Owens
2012 IEEE Transactions on Pattern Analysis and Machine Intelligence  
SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set.  ...  The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP).  ...  Cevikalp for sharing the source code of AHISD/CHISD and providing the LBP features for the Mobo dataset.  ... 
doi:10.1109/tpami.2011.283 pmid:22213765 fatcat:goxrlpqxwnhzpjb2d5rhlj6jwq

Face recognition based on regularized nearest points between image sets

Meng Yang, Pengfei Zhu, Luc Van Gool, Lei Zhang
2013 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)  
In this paper, a novel regularized nea method is proposed for image sets based face modeling an image set as a regularized affine regularized nearest points (RNP), one on each are automatically determined  ...  by an efficient ite between-set distance of RNP is then defined by the distance between the RNPs and the structu Compared with the recently developed spar nearest points (SANP) method, RNP has formulation  ...  By modeling each image set as an affine hull, Hu et al. selected two points (one point in each image set) with the closest distance as the sparse approximated nearest points (SANP), where SANPs are required  ... 
doi:10.1109/fg.2013.6553727 dblp:conf/fgr/YangZGZ13 fatcat:rjwqmebk6rc3jglz2jbeitxs2q

Learning dictionary on manifolds for image classification

Bao-Di Liu, Yu-Xiong Wang, Yu-Jin Zhang, Bin Shen
2013 Pattern Recognition  
Sparse representation algorithm combined with k-nearest neighbors is instead utilized to construct the topological structures, because it is capable of approximating the data point by selecting its homogenous  ...  The image features are approximated as a linear combination of bases selected from the dictionary in a sparse space, resulting in compact patterns.  ...  We would like to thank Qianhaoze You for the suggestion on revision of this manuscript.  ... 
doi:10.1016/j.patcog.2012.11.018 fatcat:btrvnw3u7zaihcnojrnev3gurm

Prototype Discriminative Learning for Face Image Set Classification [chapter]

Wen Wang, Ruiping Wang, Shiguang Shan, Xilin Chen
2017 Lecture Notes in Computer Science  
This paper presents a novel Prototype Discriminative Learning (PDL) method to solve the problem of face image set classification.  ...  We aim to simultaneously learn a set of prototypes for each image set and a linear discriminative transformation to make projections on the target subspace satisfy that each image set can be optimally  ...  [1] , Sparse Approximated Nearest Point (SANP) [2] , Regularized Nearest Points (RNP) [3] , Dual Linear Regression Classification (DLRC) [28] and Set-to-Set Distance Metric Learning (SSDML) [5] .  ... 
doi:10.1007/978-3-319-54187-7_23 fatcat:qnfhtjqxw5cyviss6zg3qvhpx4

Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification [article]

Muhammad Uzair, Faisal Shafait, Bernard Ghanem, Ajmal Mian
2015 arXiv   pre-print
Efficient and accurate joint representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification.  ...  Extensive experiments on a broad range of public datasets for image set classification (Honda/UCSD, CMU Mobo, YouTube Celebrities, Celebrity-1000, ETH-80) show that the proposed algorithm consistently  ...  [7] approximate each of the two nearest points between two image sets by a sparse combination of the corresponding set samples.  ... 
arXiv:1503.02445v3 fatcat:2x3pddwt3jagldi2asogmgnvlq

Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search

Damek Davis, Jonathan Balzer, Stefano Soatto
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
We introduce an asymmetric sparse approximate embedding optimized for fast kernel comparison operations arising in large-scale visual search.  ...  We evaluate our method on three benchmark image retrieval datasets: SIFT1M, ImageNet, and 80M-TinyImages.  ...  Sparse kernel approximations were recently used to provide significant speed up in image-based classification and detection tasks in [23] , where it is shown that PQ can be cast as a sparse kernel approximation  ... 
doi:10.1109/cvpr.2014.271 dblp:conf/cvpr/DavisBS14 fatcat:6q4nqsuvyrbc3hnfoeu6ci3ijm

Graph Regularized Sparse Coding for Image Representation

Miao Zheng, Jiajun Bu, Chun Chen, Can Wang, Lijun Zhang, Guang Qiu, Deng Cai
2011 IEEE Transactions on Image Processing  
The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.  ...  It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set.  ...  Image Classification For image classification, we present experiments on the benchmark USPS handwritten digits data set. 1 USPS is composed of 7291 training images and 2007 test images of size 16 16 .  ... 
doi:10.1109/tip.2010.2090535 pmid:21047712 fatcat:6rfbyic7kzf55nra5sdoydbrda

Bayesian Markov Chain Random Field Cosimulation for Improving Land Cover Classification Accuracy

Weidong Li, Chuanrong Zhang, Michael R. Willig, Dipak K. Dey, Guiling Wang, Liangzhi You
2014 Mathematical Geosciences  
It was tested using a series of expert-interpreted data sets and an image data set pre-classified by the supervised maximum likelihood (SML) algorithm. Results show that with the density  ...  This study introduces a Bayesian Markov chain random field (MCRF) cosimulation approach for improving land-use/land-cover (LULC) classification accuracy through integrating expert-interpreted data and  ...  From the dense data set, a medium data set (406 data points), a sparse data set (203 data point, Fig. 5d ), an extra-sparse data set (100 data points) and an extreme-sparse data set (50 data points) were  ... 
doi:10.1007/s11004-014-9553-y fatcat:6kgdnqny4jep3jezrvbuc457cm

Sparse Projections of Medical Images onto Manifolds [article]

George H. Chen, Christian Wachinger, Polina Golland
2013 arXiv   pre-print
Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications.  ...  We interpret our method as an out-of-sample extension that approximates kernel ridge regression.  ...  We thank Siemens Healthcare for image data.  ... 
arXiv:1303.5508v2 fatcat:xuefvzjegfgxbfo77fj3326kkq

Saliency Aware Locality-preserving Coding for Image Classification

Quan Fang, Jitao Sang, Changsheng Xu
2012 2012 IEEE International Conference on Multimedia and Expo  
The Bag-of-Features (BOF) model is widely used for image classification.  ...  However, recent locality-preserving coding schemes do not account for the saliency characteristic during the process of generating the raw image representations.  ...  Max-pooling and 2 normalization are adopted to generate the final image representation. Lib-linear SVM [13] is used for classification wherein the penalty coefficient is set to 10.  ... 
doi:10.1109/icme.2012.164 dblp:conf/icmcs/FangSX12 fatcat:ajfob5e5yrhf7k7ckr2padhjjq

A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples

Qazi Sami ul Haq, Linmi Tao, Fuchun Sun, Shiqiang Yang
2012 IEEE Transactions on Geoscience and Remote Sensing  
Extensive experiments on four real hyperspectral data sets prove that hyperspectral data is highly sparse in nature, and the proposed approaches are robust across different databases, offer more classification  ...  The proposed approaches are tested for our difficult classification problem of hyperspectral data with few labeled samples.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  ... 
doi:10.1109/tgrs.2011.2172617 fatcat:sg6xrpsxujfexlmumovqgtusp4

Palmprint Recognition Based on Image Sets [chapter]

Qingjun Liang, Lin Zhang, Hongyu Li, Jianwei Lu
2015 Lecture Notes in Computer Science  
After the feature extraction process, we use the method of sparse approximated nearest points (SANP) for palmprint image set classification.  ...  Consequently, in this paper, we present a novel approach for palmprint recognition based on image sets.  ...  There for a new term: Sparse Approximated Nearest Points (SANP) is put forward to calculate the distance between different image sets.  ... 
doi:10.1007/978-3-319-22180-9_30 fatcat:foz4dt4rzje4zkhci5zenrfv24

An enhanced sparse representation strategy for signal classification

Yin Zhou, Jinglun Gao, Kenneth E. Barner
2012 Compressive Sensing  
Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition.  ...  This assumption is reasonable for face recognition as images of the same subject under different intensity levels are still considered to be of same-class.  ...  For example, sparse representation-based classification (SRC) 4 has obtained impressive results on face recognition, which encodes a query face image over the entire set of training template images and  ... 
doi:10.1117/12.919469 fatcat:3p47xyzj7nggxb322lifsikbmq
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