Changqing Zhang

Diversity-induced Multi-view Subspace Clustering

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view representations, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods.

Low-Rank Tensor Constrained Multiview Subspace Clustering

In this paper, we explore the problem of multiview subspace clustering. We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC). Our method regards the subspace representation matrices of different views as a tensor, which captures dexterously the high order correlations underlying multiview data. Then the tensor is equipped with a low-rank constraint, which models elegantly the cross information among different views, reduces effectually the redundancy of the learned subspace representations, and improves the accuracy of clustering as well. The inference process of the affinity matrix for clustering is formulated as a tensor nuclear norm minimization problem, constrained with an additional L2,1-norm regularizer and some linear equalities. The minimization problem is convex and thus can be solved efficiently by an Augmented Lagrangian Alternating Direction Minimization (AL-ADM) method. Extensive experimental results on four benchmark datasets show the effectiveness of our proposed LT-MSC method.

Subspace clustering guided unsupervised feature selection

Unsupervised feature selection (UFS) aims to reduce the time complexity and storage burden, improve the generalization ability of learning machines by removing the redundant, irrelevant and noisy features. Due to the lack of training labels, most existing UFS methods generate the pseudo labels by spectral clustering, matrix factorization or dictionary learning, and convert UFS to a supervised problem. The learned clustering labels reflect the data distribution with respect to classes and therefore are vital to the UFS performance. In this paper, we proposed a novel subspace clustering guided unsupervised feature selection (SCUFS) method. The clustering labels of the training samples are learned by representation based subspace clustering, and features that can well preserve the cluster labels are selected. SCUFS can well learn the data distribution in that it uncovers the underlying multi-subspace structure of the data and iteratively learns the similarity matrix and clustering labels. Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods. HighlightsA novel subspace clustering guided unsupervised feature selection (SCUFS) model is proposed.SCUFS learns a similarity graph by self-representation of samples and can uncover the underlying multi-subspace structure of data.The iterative updating of similarity graph and pseudo label matrix can learn a more accurate data distribution.

Constrained Multi-View Video Face Clustering

In this paper, we focus on face clustering in videos. To promote the performance of video clustering by multiple intrinsic cues, i.e., pairwise constraints and multiple views, we propose a constrained multi-view video face clustering method under a unified graph-based model. First, unlike most existing video face clustering methods which only employ these constraints in the clustering step, we strengthen the pairwise constraints through the whole video face clustering framework, both in sparse subspace representation and spectral clustering. In the constrained sparse subspace representation, the sparse representation is forced to explore unknown relationships. In the constrained spectral clustering, the constraints are used to guide for learning more reasonable new representations. Second, our method considers both the video face pairwise constraints as well as the multi-view consistence simultaneously. In particular, the graph regularization enforces the pairwise constraints to be respected and the co-regularization penalizes the disagreement among different graphs of multiple views. Experiments on three real-world video benchmark data sets demonstrate the significant improvements of our method over the state-of-the-art methods.

Multi-label feature selection with missing labels

Abstract The consistently increasing of the feature dimension brings about great time complexity and storage burden for multi-label learning. Numerous multi-label feature selection techniques are developed to alleviate the effect of high-dimensionality. The existing multi-label feature selection algorithms assume that the labels of the training data are complete. However, this assumption does not always hold true for labeling data is costly and there is ambiguity among classes. Hence, in real-world applications, the data available usually have an incomplete set of labels. In this paper, we present a novel multi-label feature selection model under the circumstance of missing labels. With the proposed algorithm, the most discriminative features are selected and missing labels are recovered simultaneously. To remove the irrelevant and noisy features, the effective l2, p-norm (0

Salient Object Detection via Weighted Low Rank Matrix Recovery

Image-based salient object detection is a useful and important technique, which can promote the efficiency of several applications such as object detection, image classification/retrieval, object co-segmentation, and content-based image editing. In this letter, we present a novel weighted low-rank matrix recovery (WLRR) model for salient object detection. In order to facilitate efficient salient objects-background separation, a high-level background prior map is estimated by employing the property of the color, location, and boundary connectivity, and then this prior map is ensembled into a weighting matrix which indicates the likelihood that each image region belongs to the background. The final salient object detection task is formulated as the WLRR model with the weighting matrix. Both quantitative and qualitative experimental results on three challenging datasets show competitive results as compared with 24 state-of-the-art methods.

SketchNet: Sketch Classification with Web Images

In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images. In contrast to traditional approaches that use global appearance features or relay on keypoint features, our aim is to automatically learn the shared latent structures that exist between sketch images and real images, even when there are significant appearance differences across its relevant real images. To accomplish this, we propose a deep convolutional neural network, named SketchNet. We firstly develop a triplet composed of sketch, positive and negative real image as the input of our neural network. To discover the coherent visual structures between the sketch and its positive pairs, we introduce the softmax as the loss function. Then a ranking mechanism is introduced to make the positive pairs obtain a higher score comparing over negative ones to achieve robust representation. Finally, we formalize above-mentioned constrains into the unified objective function, and create an ensemble feature representation to describe the sketch images. Experiments on the TUBerlin sketch benchmark demonstrate the effectiveness of our model and show that deep feature representation brings substantial improvements over other state-of-the-art methods on sketch classification.

Flexible Multi-View Dimensionality Co-Reduction

Dimensionality reduction aims to map the high-dimensional inputs onto a low-dimensional subspace, in which the similar points are close to each other and vice versa. In this paper, we focus on unsupervised dimensionality reduction for the data with multiple views, and propose a novel method, called Multi-view Dimensionality co-Reduction. Our method flexibly exploits the complementarity of multiple views during the dimensionality reduction and respects the similarity relationships between data points across these different views. The kernel matching constraint based on Hilbert-Schmidt Independence Criterion enhances the correlations and penalizes the disagreement of different views. Specifically, our method explores the correlations within each view independently, and maximizes the dependence among different views with kernel matching jointly. Thus, the locality within each view and the consistence between different views are guaranteed in the subspaces corresponding to different views. More importantly, benefiting from the kernel matching, our method need not depend on a common low-dimensional subspace, which is critical to reduce the influence of the unbalanced dimensionalities of multiple views. Specifically, our method explicitly produces individual low-dimensional projections for individual views, which could be applied for new coming data in the out-of-sample manner. Experiments on both clustering and recognition tasks demonstrate the advantages of the proposed method over the state-of-the-art approaches.

Saliency-Aware Nonparametric Foreground Annotation Based on Weakly Labeled Data

In this paper, we focus on annotating the foreground of an image. More precisely, we predict both image-level labels (category labels) and object-level labels (locations) for objects within a target image in a unified framework. Traditional learning-based image annotation approaches are cumbersome, because they need to establish complex mathematical models and be frequently updated as the scale of training data varies considerably. Thus, we advocate the nonparametric method, which has shown potential in numerous applications and turned out to be attractive thanks to its advantages, i.e., lightweight training load and scalability. In particular, we exploit the salient object windows to describe images, which is beneficial to image retrieval and, thus, the subsequent image-level annotation and localization tasks. Our method, namely, saliency-aware nonparametric foreground annotation, is practical to alleviate the full label requirement of training data, and effectively addresses the problem of foreground annotation. The proposed method only relies on retrieval results from the image database, while pretrained object detectors are no longer necessary. Experimental results on the challenging PASCAL VOC 2007 and PASCAL VOC 2008 demonstrate the advance of our method.

Video Face Clustering via Constrained Sparse Representation

In this paper, we focus on the problem of clustering faces in videos. Different from traditional clustering on a collection of facial images, a video provides some inherent benefits: faces from a face track must belong to the same person and faces from a video frame can not be the same person. These benefits can be used to enhance the clustering performance. More precisely, we convert the above benefits into must-link and cannot-link constraints. These constraints are further effectively incorporated into our novel algorithm, Video Face Clustering via Constrained Sparse Representation (CS-VFC). The CS-VFC utilizes the constraints in two stages, including sparse representation and spectral clustering. Experiments on real-world videos show the improvements of our algorithm over the state-of-the-art methods.