Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in 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
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Representing image using the distribution of local features on a group of visual words is an effective method for visual categorization. Visual words can be related conceptually and the information can be incorporated to enhance the performance. However, conventionalmethods usually use visual words independently without considering this. This paper proposes a novel approach to measure the conceptual relation of visual words and incorporate the information into visual categorization. The conceptual relation is measured by the similarity of class distributions induced by visual words, accordingly visual words are grouped and images are represented on multiple levels. Categorization is taken using the support vector machine (SVM) with an effective kernel designed for matching multi-level representations. The proposed method is evaluated for video events categorization on the benchmark dataset and shows superior performance to conventional methods.