Multi-label classification for image annotation via sparse similarity voting

Authors:
Tomoya Sakai;Hayato Itoh;Atsushi Imiya
Affiliations:
Faculty of Engineering, Nagasaki University, Japan;Graduate School of Science and Technology, Chiba University, Japan;Institute of Media and Information Technology, Chiba University, Japan
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Year:
2010

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Abstract

We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Besides, our sparse representation-based multi-label classification can estimate a suitable combination of labels even if the combination is unlearned. Experimental results using the PASCAL dataset suggest effectiveness for image annotation compared to the existing SVM-based multi-labeling methods. Nonlinear mapping of the image representation using the kernel trick is also shown to enhance the annotation performance.