A General Two-Step Approach to Learning-Based Hashing
This work addresses the problem of developing more flexible and simpler hashing methods for machine learning practitioners, though it appears incremental as it builds on existing binary optimization and classification techniques.
The paper tackles the inflexibility and complexity of existing hashing methods by proposing a two-step framework that separates hash bit learning and hash function learning, enabling the use of various loss and hash functions. The results show that this framework outperforms state-of-the-art methods in experiments.
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.