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An adaptive color image retrieval framework using Gauss mixtures

To reduce the semantic gap, image retrieval systems based on users' relevance feedback have been adopted. However, since this structure needs human intervention during the retrieval process, it cannot be applied to fully automated systems. To avoid this problem, we propose a feed-forward framework instead of the feed-back retrieval system, which adds a classifier to the traditional system for giving feed-forward information to maximize the average precision. That is, given a database, our proposed system improves the overall precision by selecting the best mode based on known statistics (average precision vs. recall for each category). Lloyd-clustered Gauss mixtures are used in the classifier to provide the feed-forward category information and in the quantization of color images for histogram generation.