Multi-label sparse coding for automatic image annotation

Changhu Wang, Shuicheng Yan, Lei Zhang, Hong-Jiang Zhang
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
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
more » ... 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. 1643 978-1-4244-3991-1/09/$25.00 ©2009 IEEE
doi:10.1109/cvprw.2009.5206866 fatcat:ljzpfckhqvaz7c334feddwet6y