Integration of semantic and visual hashing for image retrieval

With the rapid proliferation of large-scale web images, recent years have witnessed more and more images labeled with user-provided tags, which leads to considerable effort made on hashing based image retrieval in huge databases. Current research efforts focus mostly on learning semantic hashing functions which design compact binary codes to map semantically similar images into similar codes; however the visual similarity is not well explored for constructing semantic hashing functions. Here a novel approach is proposed to learn hashing functions that preserve semantic and visual similarity between images. Specifically, semantic hashing codes are first learned by leveraging the similarity between textual structure and visual structure; then, the maximum entropy principle is exploited to achieve compact binary codes; finally, the function decay principle is introduced to remove noisy visual attributes. Experimental results conducted on a widely-used image dataset demonstrate the superior performance of the proposed method over the examined state-of-the-art techniques. (C) 2016 Published by Elsevier Inc.