Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Visual cue cluster construction via information bottleneck principle and kernel density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Recently, the bag-of-words (BOW) based image representation is getting popular in object categorization. However, there is no available visual vocabulary and it has to be learned. As to traditional learning methods, the vocabulary is constructed by exploring only one type of feature or simply concatenating all kinds of visual features into a long vector. Such constructions neglect distinct roles of different features on discriminating object categories. To address the problem, we propose a novel method to construct a conceptspecific visual vocabulary. First, we extract various visual features from local image patches, and cluster them separately according to different features to generate an initial vocabulary. Second, we formulate the concept-specific visual words selection and object categorization into a boosting framework. Experimental results on PASCAL 2006 challenge data set demonstrate the encouraging performance of the proposed method.