Biblio

With the advantage in compact representation and efficient comparison, binary hashing has been extensively investigated for approximate nearest neighbor search. In this paper, we propose a novel and general hashing framework, which simultaneously considers a new linear pair-wise distance preserving objective and point-wise constraint. The direct distance preserving objective aims to keep the linear relationships between the Euclidean distance and the Hamming distance of data points. Based on different point-wise constraints, we propose two methods to instantiate this framework. The first one is a pseudo-supervised hashing method, which uses existing unsupervised hashing methods to generate binary codes as pseudo-supervised information. The second one is an unsupervised hashing method, in which quantization loss is considered. We validate our framework on two large-scale datasets. The experiments demonstrate that our pseudo-supervised method achieves consistent improvement for the state-of-the-art unsupervised hashing methods, while our unsupervised method outperforms the state-of-the-art methods.

Recently, multimodal hashing techniques have received considerable attention due to their low storage cost and fast query speed for multimodal data retrieval. Many methods have been proposed; however, there are still some problems that need to be further considered. For example, some of these methods just use a similarity matrix for learning hash functions which will discard some useful information contained in original data; some of them relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error; some of them are not robust to noise. All these problems may degrade the performance of a model. To consider these problems, in this paper, we propose a novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH). Specifically, SRDMH tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning. In addition, it learns hashing functions and binary codes directly instead of relaxing the binary constraints so as to avoid large quantization error problem. Moreover, to make it robust and easy to solve, we further integrate a flexible l2,p loss with nonlinear kernel embedding and an intermediate presentation of each instance. Finally, an alternating algorithm is proposed to solve the optimization problem in SRDMH. Extensive experiments are conducted on three benchmark data sets. The results demonstrate that the proposed method (SRDMH) outperforms or is comparable to several state-of-the-art methods for cross-modal retrieval task.