Image search via semantic hashing learning

• Published in: 2017 29th Chinese Control And Decision Conference (CCDC) pp. 1986 - 1990
• Main Authors: Weicheng Sun, Songhao Zhu, Yanyun Cheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Hashing Code; Image Retrieval; Semantic Similarity; Visual Structure
• ISSN: 1948-9447
• DOI: 10.1109/CCDC.2017.7978842

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 designs compact binary codes to map semantically similar images to similar codes, and 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, maximum entropy principle is exploited to achieve compact binary codes; finally, function decay principle is introduced to remove noisy visual attributes. Experimental results conducted on a widely-used image dataset demonstrate the proposed approach can effectively improve the performance in image retrieval.