Rank-based distance metric learning: An application to image retrieval
We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance metric learning. However, the capacity of existing algorithms is limited, because they usually assume that the distance between two similar objects is smaller than the distance between two dissimilar objects. This assumption may not hold, especially in the case of information retrieval when the input space is heterogeneous. To address this problem explicitly, we propose rank-based distance metric learning. Our approach overcomes the drawback of existing algorithms by comparing the distances only among the relevant and irrelevant objects for a given query. To avoid over-fitting, a regularizer based on the Burg matrix divergence is also introduced. We apply the proposed framework to tattoo image retrieval in forensics and law enforcement application domain. The goal of the application is to retrieve tattoo images from a gallery database that are visually similar to a tattoo found on a suspect or a victim. The experimental results show encouraging results in comparison to the standard approaches for distance metric learning.