learning image semantics from users relevance feedback
In this paper, a learning method is proposed to improve the retrieval process in image databases. This method uses the search transaction logs in the system and user relevance feedback scores to create a semantic space of the image database. The semantic space includes many semantic classes and all the images in the database are clustered to these classes with different membership values. The sparsity problem in the transaction logs data is solved by filling the missing values by an estimation based on the image contents and image similarities. A Fuzzy clustering algorithm is developed to create the semantic classes and find image memberships in the classes.
image semantics clustering with missing values image retrieval