Self-Adaptive Hashing for Fine-Grained Image Retrieval

The main challenge of fine-grained image hashing is how to learn highly discriminative hash codes to distinguish the within and between class variations. On the one hand, most of the existing methods treat sample pairs as equivalent in hash learning, ignoring the more discriminative information contained in hard sample pairs. On the other hand, in the testing phase, these methods ignore the influence of outliers on retrieval performance. In order to solve the above issues, this paper proposes a novel Self-Adaptive Hashing method, which learns discriminative hash codes by mining hard sample pairs, and improves retrieval performance by correcting outliers in the testing phase. In particular, to improve the discriminability of hash codes, a pair-weighted based loss function is proposed to enhance the learning of hash functions of hard sample pairs. Furthermore, in the testing phase, a self-adaptive module is proposed to discover and correct outliers by generating self-adaptive boundaries, thereby improving the retrieval performance. Experimental results on two widely-used fine-grained datasets demonstrate the effectiveness of the proposed method.