Filter object categories using coboost with 1st and 2nd order features

Authors:
Xi Liu;Zhiping Shi;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Bei ...;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:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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 lack of labeled object images. Our method deals with it by combining a co-training algorithm CoBoost [7] with two features - 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively. We iteratively train two boosting classifiers based on the 1st and 2nd order features, during which each classifier provides labeled data for the other classifier. It is effective because the 1st and 2nd order features make up an independent and redundant feature split. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art.