Weighted Gaussian Loss based Hamming Hashing

Abstract

Recently, deep Hamming hashing methods have been proposed for Hamming space retrieval which enables constant-time search by hash table lookups instead of linear scan. When carrying out Hamming space retrieval, for each query datapoint, there is a Hamming ball centered on the query datapoint, and only the datapoints within the Hamming ball are returned as the relevant ones, while those beyond are discarded directly. Thus, to further enhance the retrieval performance, it is a key point for the Hamming hashing methods to decrease the dissimilar datapoints within the Hamming ball. However, nearly all existing Hamming hashing methods cannot effectively penalize the dissimilar pairs within the Hamming ball to push them out. To tackle this problem, in this paper, we propose a novel Weighted Gaussian Loss based Hamming Hashing, called WGLHH, which introduces a weighted Gaussian loss to optimize hashing model. Specifically, the weighted Gaussian loss consists of three parts: a novel Gaussian-distribution based loss, a novel badly-trained-pair attention mechanism and a quantization loss. The Gaussian-distribution based loss is proposed to effectively penalize the dissimilar pairs within the Hamming ball. The badly-trained-pair attention mechanism is proposed to assign a weight for each data pair, which puts more weight on data pairs whose corresponding hash codes cannot preserve original similarity well, and less on those having already handled well. The quantization loss is used to reduce the quantization error. By incorporating the three parts, the proposed weighted Gaussian loss will penalize significantly on the dissimilar pairs within the Hamming ball to generate more compact hashing codes. Extensive experiments on two benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in image retrieval task.