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|Title:||Multi-label sparse coding for automatic image annotation|
|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|
|Appears in Collections:||Staff Publications|
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