Learning image similarities and categories from content analysis and relevance feedback

In this work, a scheme that learns image similarities and categories from relevance feedback is presented. First, we choose the most suitable features to describe images by content analysis and categorize each image by predicting its semantic meanings. During the retrieval process, users are allowed to confirm semantic classification of the query example and evaluate retrieval results with relevance feedback. By analyzing the feedback information, the system learns both image similarities and semantic meanings. In similarity learning, the retrieving results are refined by modifying the similarity metric. Semantic learning is performed by using the decision tree training algorithm.