Cross-Media Retrieval via Semantic Entity Projection

Cross-media retrieval is becoming increasingly important nowadays. To address this challenging problem, most existing approaches project heterogeneous features into a unified feature space to facilitate their similarity computation. However, this unified feature space usually has no explicit semantic meanings, which might ignore the hints contained in the original media content, and thus is not able to fully measure the similarities among different media types. By considering the above issues, we propose a new approach to cross-media retrieval via semantic entity projection (SEP) in this paper. Our approach consists of three main steps. Firstly, an entity level with fine-grained semantics between low-level features and high-level concepts are constructed, so as to help bridge the semantic gap to a certain extent. Then, an entity projection is learned by minimizing both cross-media correlation error and single-media reconstruction error from low-level features to the entity level, with which a unified feature space with explicit semantic meanings can be obtained from low-level features. Finally, the semantic abstraction of high-level concepts is generated by using logistic regression to conduct cross-media retrieval. Experimental results on the Wikipedia dataset show the effectiveness of the proposed approach.