2013/09/07 by Guosheng Lin, Chunhua Shen, Lin, Guosheng +5
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Video Surveillance and Tracking Methods #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1309.1853
13 pages. Appearing in Int. Conf. Computer Vision (ICCV) 2013
arxiv created 2013/09/07 · openalex publication_date 2013/09/07 · arxiv updated 2013/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art.