Filter object categories: employing visual consistency and semi-supervised approach

Authors:
Xi Liu;Zhixin Li;Zhiping Shi;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences and Graduate University of Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

We describe a method for filtering object category from a large number of noisy images. This problem is particularly difficult due to the greater variation within object categories and only a few labeled object images available. Our method deals with it by using visual consistency and semi-supervised approach. The images of one category often share some visual consistency so that the most irrelevant images can be first removed. Among the left images, a voting method is used to obtain more object exemplars with the initial object exemplars manually selected by users. Finally with all the obtained exemplars and those unlabeled images, we create a semi-supervised classifier to rank all the images. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art. Besides, we collect five more categories from Google Images to show the effectiveness of the method.