Book ChapterDOI
Kernel sparse representation for image classification and face recognition
Shenghua Gao,Ivor W. Tsang,Liang-Tien Chia +2 more
- pp 1-14
Reads0
Chats0
TLDR
KSR is essentially the sparse coding technique in a high dimensional feature space mapped by implicit mapping function that outperforms sparse coding and EMK, and achieves state-of-the-art performance for image classification and face recognition on publicly available datasets.Abstract:
Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dimensional feature space mapped by implicit mapping function. We apply KSR to both image classification and face recognition. By incorporating KSR into Spatial Pyramid Matching(SPM), we propose KSRSPM for image classification. KSRSPM can further reduce the information loss in feature quantization step compared with Spatial Pyramid Matching using Sparse Coding(ScSPM). KSRSPM can be both regarded as the generalization of Efficient Match Kernel(EMK) and an extension of ScSPM. Compared with sparse coding, KSR can learn more discriminative sparse codes for face recognition. Extensive experimental results show that KSR outperforms sparse coding and EMK, and achieves state-of-the-art performance for image classification and face recognition on publicly available datasets.read more
Citations
More filters
Proceedings ArticleDOI
Sparse representation or collaborative representation: Which helps face recognition?
TL;DR: This paper indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification, and proposes a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS).
Journal ArticleDOI
A Survey of Sparse Representation: Algorithms and Applications
TL;DR: A comprehensive overview of sparse representation is provided and an experimentally comparative study of these sparse representation algorithms was presented, which could sufficiently reveal the potential nature of the sparse representation theory.
Journal Article
Dictionary Learning
Ivana Tosic,Pascal Frossard +1 more
TL;DR: Methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data are described and dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification.
Journal ArticleDOI
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding
Jun Yu,Yong Rui,Dacheng Tao +2 more
TL;DR: A multimodal hypergraph learning-based sparse coding method is proposed for image click prediction, and the obtained click data is applied to the reranking of images, which shows the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.
Journal ArticleDOI
Hyperspectral Image Classification via Kernel Sparse Representation
TL;DR: Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.