Learning to Hash with Partial Tags: Exploring Correlation between Tags and Hashing Bits for Large Scale Image Retrieval

Similarity search is an important technique in many large scale vision applications. Hashing approach becomes popular for similarity search due to its computational and memory efficiency. Recently, it has been shown that the hashing quality could be improved by combining supervised information, e.g. semantic tags/labels, into hashing function learning. However, tag information is not fully exploited in existing unsupervised and supervised hashing methods especially when only partial tags are available. This paper proposes a novel semi-supervised tag hashing (SSTH) approach that fully incorporates tag information into learning effective hashing function by exploring the correlation between tags and hashing bits. The hashing function is learned in a unified learning framework by simultaneously ensuring the tag consistency and preserving the similarities between image examples. An iterative coordinate descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error. Extensive experiments on two large scale image datasets demonstrate the superior performance of the proposed approach over several state-of-the-art hashing methods.