Two-Stage Semantic Matching for Cross-Media Retrieval

摘要

With the development of information technology, there exists a large amount of multi-media data in our lives; the data is heterogeneous with low-level features while consistent with semantic information. Traditional mono-media retrieval can't cross the heterogeneous gap of multi-media data, and cross-media retrieval is arousing many researchers' interests. In this paper, we propose a two-stage semantic matching for cross-media retrieval based on support vector machines (called TSMCR). Our approach uses a combination of testing images' predictive labels and testing texts' predictive labels as the next training labels. It makes full use of semantic information of both training samples and testing samples, and the experimental results on four state-of-the-art datasets show that the TSMCR algorithm is effective.