Improving the similarity estimation via score distribution
Generally distance-based similarity estimation between two images is not always reliable due to the limitations in both image understanding techniques and distance measure methods. This paper presents a novel approach for improving the similarity estimation through introducing the distribution information of similarity scores. The key idea is based on an underlying assumption that the distributions of similarity scores are similar for true-relevant images when they query an independent database. By representing each distribution with the area under the corresponding similarity score curve, the difference between different distributions can be easily calculated and employed to update the original distance measure. Experiments on three public datasets with various feature representations show that the enhanced similarity estimation remarkably outperforms the original distance measure and the proposed approach also keeps a good generalization ability on various datasets and feature representations.