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Extracting shared subspace incrementally for multi-label image classification

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Abstract

With the popularity of internet technology, thousands of new images with multiple labels appear on the web every day. For a large number of images updated daily on the websites, it is of ever-increasing importance to classify these new multi-label images online in real time. Accordingly, this paper presents an incremental shared subspace learning method for multi-label image classification. With the incremental lossless matrix factorization, the proposed algorithm can be incrementally performed without using original existing input data, thus high computational complexity involved in extracting the shared subspace can be avoided. Several publicly available multi-label image datasets are used to evaluate the proposed method. Experimental results demonstrate that the proposed approach is much more efficient than the non-incremental methods without decreasing the classification performance.

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  1. http://www.public.asu.edu/~sji03/multilabel/.

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Acknowledgments

This work was supported in part by 973 Program (No. 2012CB316400), National Natural Science Foundation of China (No. 61025013, No. 61172129), PCSIRT (No. 201206), NCET (No. 13-0661, No. 4112043) and Fundamental Research Funds for the Central Universities (No. 2012JBZ012).

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, China

    Lei Zhang, Yao Zhao & Zhenfeng Zhu

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing , 100044, China

    Lei Zhang, Yao Zhao & Zhenfeng Zhu

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  1. Lei Zhang
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  2. Yao Zhao
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  3. Zhenfeng Zhu
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Correspondence to Yao Zhao.

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Zhang, L., Zhao, Y. & Zhu, Z. Extracting shared subspace incrementally for multi-label image classification. Vis Comput 30, 1359–1371 (2014). https://doi.org/10.1007/s00371-013-0891-4

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