Combining supervised learning with color correlograms for content-based image retrieval

The paper addresses how relevance feedback can be used to improve the performance of content-based image retrieval. We present two supervised learning methods: learning the query and learning the metric. We combine the learning methods with the recently proposed color correlograms for image indexing/retrieval. Our results on a large image database of over 20, 000 images suggest that these learning methods are quite effective for content-based image retrieval.