Visual categorization with negative examples for free

12 years 10 months ago
Visual categorization with negative examples for free
Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is becoming a novel yet subjective and inaccurate source of learning examples. Different from existing work focusing on collecting positive examples, we study in this paper the potential of substituting social tagging for expert labeling for creating negative examples. We present an empirical study using 6.5 million Flickr photos as a source of social tagging. Our experiments on the PASCAL VOC challenge 2008 show that with a relative loss of only 4.3% in terms of mean average precision, expert-labeled negative examples can be completely replaced by social-tagged negative examples for consumer photo categorization. Categories and Subject Descriptors I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Object recognition; H.2.4 [Database Management]: Multimedia databases General Terms Algorithms, Measur...
Xirong Li, Cees G. M. Snoek
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where MM
Authors Xirong Li, Cees G. M. Snoek
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