Adaptive Bit Selection for Scalable Deep Hashing

Abstract

Deep Hashing is one of the most important methods for generating compact feature representation in content-based image retrieval. However, in various application scenarios, it requires training different models with diversified memory and computational resource costs. To address this problem, in this paper, we propose a new scalable deep hashing framework, which aims to generate binary codes with different code lengths by adaptive bit selection. Specifically, the proposed framework consists of two alternative steps, i.e., bit pool generation and adaptive bit selection. In the first step, a deep feature extraction model is trained to output binary codes by optimizing retrieval performance and bit properties. In the second step, we select informative bits from the generated bit pool with reinforcement learning algorithm, in which the same retrieval performance and bit properties are directly used in computing reward. The bit pool can be further updated by fine-tuning the deep feature extraction model with more attention on the selected bits. Hence, these two steps are alternatively iterated until convergence is achieved. Notably, most existing binary hashing methods can be readily integrated into our framework to generate scalable binary codes. Experiments on four public image datasets prove the effectiveness of the proposed framework for image retrieval tasks.