Strategies for positive and negative relevance feedback in image retrieval

Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. It has also been increasingly used in content-based image retrieval and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings. This paper compares a variety of strategies for positive and negative feedback. The performance evaluation of feedback algorithms is a hard problem. To solve this, we obtain judgments from several users and employ an automated feedback scheme. We then evaluate different techniques using the same judgements. Using automated feedback, the ability of a system to adapt to the user's needs can be measured very effectively. Our study highlights the utility of negative feedback, especially over several feedback steps.