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Multi-Label Sparse Coding for Automatic Image Annotation

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Multi-Label Sparse Coding for Automatic Image Annotation
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.
Changhu Wang (University of Science and Technology
Added 06 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Changhu Wang (University of Science and Technology of China), Shuicheng Yan (National University of Singapore), Lei Zhang (Microsoft Research Asia), Hong-Jiang Zhang (Microsoft Corporation)
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