Semantic Based Image Retrieval Using Relevance Feedback

In this paper, we propose a method for image categorization and retrieval, by integrating knowledge from low-level and semantic features extracted from images. The low -level descriptors, like color, position, dimension and texture are extracted from each image region. These mathematical descriptors are automatically associated with intermediate semantic descriptors. The intermediate descriptors are used also for image categorization and for qualitative definition of semantic keywords in the user queries. For improving the initial query results, we apply a relevance feedback mechanism that uses the low -level descriptors of the images selected as relevant by user for producing the final query results. A support vector machine classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier and the semantic indexing, we implement a software system that can retrieve more images relevant to the query in the database efficiently.