Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Abstract

The binary coding technique has been widely used in approximate nearest neighbors (ANN) search tasks. Traditional hashing algorithms treat binary bits equally, which usually causes an ambiguous ranking. To solve this issue, we propose an innovative bitwise weight method dubbed minimal residual ordinal loss hashing (MROLH). Different from a two-step mechanism, MROLH simultaneously learns binary codes and bitwise weights by a feedback mechanism. When the algorithm converges, the binary codes and bitwise weights can be well adaptive to each other. Furthermore, we establish the ordinal relation preserving constraint based on quartic samples to enhance the power of preserving relative similarity. To decrease the training complexity, we utilize a tensor ordinal graph to represent quartic ordinal relation, and the original objective function is approximated by the one based on triplet samples. In this paper, we also assign different weight values to training samples. During the training procedure, the weight of each data is initialized to the same value, and we iteratively boost the weight of the data whose relative similarity is not well preserved. As a result, we can minimize the residual ordinal loss. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed method MROLH achieves a superior ANN search performance in both the Hamming space and the weighted Hamming space over the sate-of-the-art approaches.