Beyond bag of words: image representation in sub-semantic space
Proceedings of the 21st ACM international conference on Multimedia
Undo the codebook bias by linear transformation for visual applications
Proceedings of the 21st ACM international conference on Multimedia
Object class detection: A survey
ACM Computing Surveys (CSUR)
Laplacian affine sparse coding with tilt and orientation consistency for image classification
Journal of Visual Communication and Image Representation
A robust image classification scheme with sparse coding and multiple kernel learning
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Engineering Applications of Artificial Intelligence
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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We propose an image classification framework by leveraging the non-negative sparse coding, low-rank and sparse matrix decomposition techniques (LR-Sc^+ SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc^+ SPM) to extract local features' information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, motivated by the observation that images of the same class often contain correlated (or common) items and specific (or noisy) items, we propose to leverage the low-rank and sparse matrix recovery technique to decompose the feature vectors of images per class into a low-rank matrix and a sparse error matrix. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc^+ SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is leveraged for the final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks.