Kernel Canonical Correlation with Similarity Refinement for Automatic Image Tagging
Automatic image tagging (AIT) is an effective technology to facilitate the process of image retrieval without requiring user to provide a retrieval instance beforehand. In this paper, we propose an AIT method based on kernel canonical correlation analysis (KCCA) with similarity refinement (KCCSR). As a statistic correlation technique, the KCCA aims at extracting some kind of hidden information shared commonly by the two random variables. Different from the previous KCCA based tagging methods, the graph based similarity refinements are first implemented by an interactive way to obtain the enhanced visual and textual representations. Subsequently, the KCCA is applied to them to mine the unique intrinsic semantic representation space, in which the AIT can be completed. The final experimental results validate the effectiveness of the proposed KCCSR.