| 2004 IEEE International Conference on Multimedia and Expo (ICME) Online Feature Selection Based on Generalized Feature Contrast Model* (2008) | |||||||||||||||
Abstract | |||||||||||||||
| To really bridge the gap between high-level semantics and low-levelfeatures in content-based image retrieval (CBIR), a problem that must be solved is: which features are suit-able for explaining the current query concept. In thispopec we propose a novel feature selection criterion based on a psychological similarity measurement- generalized feature contrast model, and implement an online feature selection algorithm in a boosting manner to select the most repre-sentative features and do classijication during each feed-back round. The advantage of the proposed method is: it doesn’t require Gaussian assumption for “relevant ” images as other online FS methods; it accounts for the intrinsic asymmetry between “relevant ” and “irrelevant ” image sets in CBIR online Ieaming; it is very fast. Extensive expen-ment5 have shown our algorithm’s effectiveness. 1 | |||||||||||||||
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