The multi-view subspace clustering attracts much attention in recent years. Most methods follow the framework of fusing the affinity graph learned in each view. In this framework, both the fusion strategy and built graph of each view are very important. In this paper, we propose novel methods for multi-view subspace clustering to address these two aspects. On the one hand, we adopt the autoencoders with Laplacian regularization to construct the affinity graph in each view. Compared with previous work employing the autoencoders, the Laplacian term in our method can guide the learned latent representation favoring affinity extraction. Besides, we also discuss the reasons for adding Laplacian regularization. On the other hand, we propose a novel fusion strategy distinguished from the related literature. If the affinity graph of some view is not extracted well, the performance of previous fusion strategies will be seriously affected. Since our strategy can choose the best part from each affinity graph, it can overcome this limitation to some extent. Extensive experimental results on multiple benchmark data sets confirm the effectiveness of our method.

Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time.

Region-based image retrieval and relevance feedback are two important methods to bridge the gap between the low-level visual features and the high-level semantic concepts in content-based image retrieval. In this paper, we address the issue of introducing the relevance feedback mechanism into the region-based image retrieval with online feature selection during each feedback round. Our contribution is two-fold. (1) A novel region-based image representation is proposed. Based on a generative model, a fuzzy codebook is extracted from the original region-based features, which represents the images in a uniform real-value feature space. (2) A feature selection criterion is developed and an effective relevance feedback algorithm is implemented in a boosting manner to simultaneously select the optimal features from the fuzzy codebook, and generate a strong ensemble classifier over the selected features. Experimental results show that the proposed scheme can substantially improve the retrieval performance of the region-based image retrieval system.

Content-based image retrieval can be viewed as a classification problem, and the small sample size leaning difficulty makes it difficult for most CBIR classifiers to get satisfactory performance. In this paper, using the SVM classifier as the component classifier, the method of ensemble of classifiers is incorporated into the relevance feedback process to alleviate this problem from two aspects: (1) within each feedback round, multiple parallel component classifiers are constructed, one over one feature subspace individually, and then are merged together to get an ensemble classifier; (2) during feedback rounds, a boosting method is incorporated to sequentially combine the component classifiers over each feature subspace respectively, which further improves the classification result. Experiments over 5000 images show that the proposed method can improve the retrieval performance consistently, without loss of efficiency.

Long-term relevance feedback learning is an important learning mechanism in content-based image retrieval. In this paper, our work has two contributions: (1) A multilayer semantic representation (MSR) is proposed and an algorithm is implemented to automatically build the MSR for image database through long-term relevance feedback learning. (2) The accumulated MSR is incorporated with the short-term feedback learning to help subsequent users' retrieval. The MSR memorizes the multicorrelation among images and integrates these memories to build hidden semantic concepts for images, which are distributed in multiple semantic layers. In experiment, an MSR is built based on the real retrieval from 10 different users, which can precisely describe the hidden concepts underlying images and help to bridge the gap between high-level concepts and low-level features and thus improve the retrieval performance significantly.