Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPRW.2009.5206866
Title: Multi-label sparse coding for automatic image annotation
Authors: Wang, C.
Yan, S. 
Zhang, L.
Zhang, H.-J.
Issue Date: 2009
Source: Wang, C.,Yan, S.,Zhang, L.,Zhang, H.-J. (2009). Multi-label sparse coding for automatic image annotation. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 : 1643-1650. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPRW.2009.5206866
Abstract: In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is encoded into a so-called supervector, derived from the universal Gaussian Mixture Models on orderless image patches. Then, a label sparse coding based subspace learning algorithm is derived to effectively harness multilabel information for dimensionality reduction. Finally, the sparse coding method for multi-label data is proposed to propagate the multi-labels of the training images to the query image with the sparse 1 reconstruction coefficients. Extensive image annotation experiments on the Corel5k and Corel30k databases both show the superior performance of the proposed multi-label sparse coding framework over the state-of-the-art algorithms. ©2009 IEEE.
Source Title: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/71041
ISBN: 9781424439935
DOI: 10.1109/CVPRW.2009.5206866
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