Evidence combination for multi-point query learning in content-based image retrieval

In multipoint query learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multipoint query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multipoint queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multipoint queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa.