Jointly Sparse Hashing for Image Retrieval.

Abstract

Recently, hash learning attracts great attentions since it can obtain fast image retrieval on large-scale datasets by using a series of discriminative binary codes. The popular methods include manifold-based hashing methods, which aim to learn the binary codes by embedding the original high-dimensional data into low-dimensional intrinsic subspace. However, most of these methods tend to relax the discrete constraint to compute the final binary codes in an easier way. Therefore, the information loss will increase. In this paper, we propose a novel jointly sparse regression model to minimize the locality information loss and obtain jointly sparse hashing method. The proposed model integrates locality, joint sparsity and rotation operation together with a seamless formulation. Thus, the drawback in previous methods using two separated and independent stages such as PCA-ITQ and the similar methods can be addressed. Moreover, since we introduce the joint sparsity, the feature extraction and jointly sparse feature selection can also be realized in a single projection operation, which has the potentials to select more discriminant features. The convergence of the proposed algorithm is proved, and the essences of the iterative procedures are also revealed. The experimental results on large-scale datasets demonstrate the performance of the proposed method.