Multi-view clustering has attracted increasing attention in recent years. However, most existing multi-view clustering approaches are performed in a purely unsupervised manner, while ignoring the valuable weak supervision information that can be obtained (e.g., active query) in many real applications. This paper considers the weak pairwise constraints among samples to enhance the clustering performance, and proposes a Semi-supervised Multi-view Clustering method with Active Constraints, SMCAC for short. SMCAC consists of two stages, clustering (C-stage) and active query (A-stage). In the C-stage, we design a tensor based multi-view graph learning model equipped with sample pairwise constraints regularization to facilitate the discriminative graph learning and fusion. An effective optimization algorithm based on alternating direction minimization is devised to solve the clustering model. In the A-stage, the most uncertain or difficult sample pairs are actively selected to query the constraints, based on the divergence of multi-view similarities learned in the C-stage. The two processes alternate iteratively until the maximum number of queries is reached. Extensive experiments on several popular datasets well validate the effectiveness of the proposed method.

Hashing methods have recently attracted extensive attention in cross-modal retrieval. Most supervised hashing methods attempt to preserve the semantic information into hash codes by leveraging the original logical label matrix. However, they generally treat all labels equally, and ignore the relative significance of different labels due to the variety of data features. In this article, we argue that exploring the relative importance of labels benefits the enhancement of semantic information, and we propose a novel LAbel Distribution Guided Hashing (LADH) method for cross-modal retrieval. In particular, LADH first learns a feature-induced label distribution for each sample to weigh different labels, which leverages the multi-modal feature information to enrich the semantic label information. By jointly using the learned label distributions and multi-modal features, the latent representation and hash codes are obtained with multi-modal feature selection and enhanced semantic similarities embedded. An efficient algorithm is designed to solve the proposed method whose time complexity is linear to the number of the training instances. Experimental results on several public benchmark datasets verify the effectiveness and efficiency of our method compared with the state-of-the-art methods.

Hashing methods have captured much attention for cross-modal retrieval in recent years. Most existing approaches mainly focus on preserving the semantic similarity across heterogeneous modalities in a shared Hamming subspace, while the label information and potential correlations of multi-label semantics are not fully excavated. In this article, a novel Adaptive Label correlation based asymmEtric Cross-modal Hashing method, i.e., ALECH, is proposed for cross-modal retrieval. ALECH decomposes hash learning into two steps, hash codes learning and hash functions learning. For hash codes learning, the high-order semantic label correlations are adaptively exploited to guide the latent feature learning, while simultaneously generating the binary codes in a discrete manner. The asymmetric strategy is utilized to connect the latent feature space and Hamming space, and preserve the pairwise semantic similarity. Different from other two-step methods that directly adopt simple least-squares regression to learn hash functions based on binary codes, ALECH leverages both hash codes and semantic labels for hash functions learning which further preserves the similarity. Experiments on several benchmark datasets demonstrate that the proposed ALECH method outperforms the state-of-the-art cross-hashing methods.

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised Cross-Modal Hashing (CMH) methods over unsupervised ones. Nevertheless, most existing supervised CMH methods still suffer from some limitations: 1) it is assumed that the observed labels of training data are complete and accurate, which may be impractical due to the missing and wrong class assignments in real applications, and 2) the semantic information is not fully excavated, especially for the semantic correlations among labels. To address these issues, this paper proposes a Weakly-supervised enhAnced Semantic-aware Hashing (WASH) method which simultaneously estimates the label noises and performs enhanced semantic-aware hash learning. WASH employs the low-rank and sparse decomposition to alleviate the label noises, and a high-level semantic factor as well as a semantic correlation matrix is obtained by low-rank factorization on the noise-reduced labels. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce the heterogeneity gaps, so as to enhance the semantic awareness of shared representation. In this way, the hash codes can be obtained by binarizing the shared representation with pairwise semantic similarity preserved. Experiments on several benchmark datasets verify the effectiveness of the proposed method in comparison with the state-of-the-art CMH approaches.