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This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition,...
In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text...
Conventional multiple-instance learning (MIL) algorithms for image annotation usually neglect concept dependence (i.e., the relationship between positive and negative concepts) and feature selection (i.e., which feature modality is suitable for a specific concept) problems, which have significant influence on the annotation performance. In this paper, we propose a novel concept-dependent algorithm...
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