Learning weighted distances for relevance feedback in image retrieval

Abstract

We present a new method for relevance feedback in image retrieval and a scheme to learn weighted distances which can be used in combination with different relevance feedback methods. User feedback is a crucial step in image retrieval to maximise retrieval performance as was shown in recent image retrieval evaluations. Machine learning is expected to be able to learn how to rank images according to users needs. Most image retrieval systems incorporate user feedback using rather heuristic means and only few groups have formally investigated how to maximise the benefit from it using machine learning techniques. We incorporate our distance-learning method into our new relevance feedback scheme and into two different approaches from the literature. The methods are compared on two publicly available databases, one which is purely content-based and one which uses additional textual information. It is shown that the new relevance feedback scheme outperforms the other methods and that all methods benefit from weighted distance learning.