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Multi-label classification for image annotation via sparse similarity voting


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Title: Multi-label classification for image annotation via sparse similarity voting
Authors: Sakai, Tomoya / Itoh, Hayato / Imiya, Atsushi
Issue Date: 2011
Publisher: Springer Verlag
Citation: Lecture Notes in Computer Science, 6469(2), pp.344-353; 2011
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.
Description: International Workshops on Computer Vision, ACCV 2010; Queenstown; 8 November 2010 through 9 November 2010
URI: http://hdl.handle.net/10069/27087
ISSN: 03029743
Rights: © 2011 Springer-Verlag Berlin Heidelberg. / The original publication is available at www.springerlink.com
Type: Conference Paper
Text Version: author
Appears in Collections:Conference Paper

Citable URI : http://hdl.handle.net/10069/27087

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