Abstract
Hashing has been widely exploited for information retrieval recently, because of its high computation efficiency and low storage cost. However, many existing hashing methods cannot perform well on large-scale social image retrieval, due to the relaxed hash optimization and the lack of supervised semantic labels. In this paper, we propose an efficient Weakly-supervised Discrete Hashing (WDH) to solve the limitations. We formulate a unified weakly-supervised hash learning framework. It could effectively enrich the semantics of image hash codes with the freely obtained user-provided social tags and simultaneously remove their involved adverse noises. Furthermore, instead of relaxed hash optimization, we propose an efficient discrete hash optimization method based on Augmented Lagrangian Multiplier (ALM) to directly solve the hash codes without quantization information loss. Experiments on two standard social image datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art hashing techniques.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
