Interactive learning of image visual similarities and semantic categorization

Zijun Yang; C.-C. Jay Kuo

doi:10.1117/12.403821

11 October 2000 Interactive learning of image visual similarities and semantic categorization

Zijun Yang, C.-C. Jay Kuo

Proceedings Volume 4210, Internet Multimedia Management Systems; (2000) https://doi.org/10.1117/12.403821
Event: Information Technologies 2000, 2000, Boston, MA, United States

Abstract

The query by example model has been extensively used to retrieve similar images in content-based image database management. The query is characterized by searching images with feature vectors similar to those of the example based upon either a default of a user-defined similarity metric. However, low level features often encounter a severe performance bottleneck as applied to natural image collections with complicated contents and great perceptual varieties. The feature-based similarity matching approach tends to retrieve many irrelevant images. This is not surprising since images different in semantic meanings but close enough in low level features can be returned as pertinent result. Such a query process lacks user involvement and therefore results in a gap between features and semantics.

Citation Download Citation

Zijun Yang and C.-C. Jay Kuo "Interactive learning of image visual similarities and semantic categorization", Proc. SPIE 4210, Internet Multimedia Management Systems, (11 October 2000); https://doi.org/10.1117/12.403821

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