Effective Heterogeneous Similarity Measure with Nearest Neighbors for Cross-Media Retrieval

Emerging multimedia content including images and texts are always jointly utilized to describe the same semantics. As a result, crossmedia retrieval becomes increasingly important, which is able to retrieve the results of the same semantics with the query but with different media types. In this paper, we propose a novel heterogeneous similarity measure with nearest neighbors (HSNN). Unlike traditional similarity measures which are limited in homogeneous feature space, HSNN could compute the similarity between media objects with different media types. The heterogeneous similarity is obtained by computing the probability for two media objects belonging to the same semantic category. The probability is achieved by analyzing the homogeneous nearest neighbors of each media object. HSNN is flexible so that any traditional similarity measure could be incorporated, which is further regarded as the weak ranker. An effective ranking model is learned from multiple weak rankers through AdaRank for cross-media retrieval. Experiments on the wikipedia dataset show the effectiveness of the proposed approach, compared with stateof-the-art methods. The cross-media retrieval also shows to outperform image retrieval systems on a unimedia retrieval task.