Adaptive Bit Selection via Deep Reinforcement Learning for Large-Scale Image Hashing

Image hashing enables efficient large-scale image retrieval by encoding high-dimensional visual data into compact binary representations. However, existing deep hashing methods typically learn fixed-length hash codes in a fully supervised manner, often generating redundant bits that limit discriminative capability and increase storage overhead. In this paper, we propose a deep reinforcement learning-based adaptive bit selection framework for compact image hashing. We formulate hash refinement as a Markov Decision Process (MDP) and employ a Proximal Policy Optimization (PPO) agent to selectively retain the most informative hash bits while discarding redundant ones, directly optimizing retrieval performance through mean Average Precision (mAP). The proposed approach integrates CNN-based hash extraction with reinforcement-driven adaptive regeneration, producing compact yet highly discriminative binary codes. Extensive experiments on standard image retrieval benchmarks demonstrate consistent improvements over state-of-the-art deep hashing methods in terms of retrieval accuracy and efficiency, highlighting the effectiveness of reinforcement learning for adaptive representation learning in intelligent large-scale retrieval systems.