Learning a Probabilistic Topology Discovering Model for Scene Categorization

Image-Specific Classification With Local and Global Discriminations

Most image classification methods try to learn classifiers for each class using training images alone. Due to the interclass and intraclass variations, it would be more effective to take the testing images into consideration for classifier learning. In this brief, we propose a novel image-specific classification method by combing the local and global discriminations of training images. We adaptively train classifier for each testing image instead of generating classifiers for each class with training images alone. For each testing image, we first select its ${k}$ nearest neighbors in the training set with the corresponding labels for local classifier training. This helps to model the distinctive characters of each testing image. Besides, we also use all the training images for global discrimination modeling. The local and global discriminations are combined for final classification. In this way, we could not only model the specific character of each testing image but also avoid the local optimum by jointly considering all the training images. To evaluate the usefulness of the proposed image-specific classification with local and global discrimination (ISC-LG) method, we conduct image classification experiments on several public image data sets. The superior performances over other baseline methods prove the effectiveness of the proposed ISC-LG method.

Patch Alignment Manifold Matting

Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results using subspace reconstructing error. Furthermore, we utilize Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting. The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.

Scene Parsing From an MAP Perspective

Scene parsing is an important problem in the field of computer vision. Though many existing scene parsing approaches have obtained encouraging results, they fail to overcome within-category inconsistency and intercategory similarity of superpixels. To reduce the aforementioned problem, a novel method is proposed in this paper. The proposed approach consists of three main steps: 1) posterior category probability density function (PDF) is learned by an efficient low-rank representation classifier (LRRC); 2) prior contextual constraint PDF on the map of pixel categories is learned by Markov random fields; and 3) final parsing results are yielded up to the maximum a posterior process based on the two learned PDFs. In this case, the nature of being both dense for within-category affinities and almost zeros for intercategory affinities is integrated into our approach by using LRRC to model the posterior category PDF. Meanwhile, the contextual priori generated by modeling the prior contextual constraint PDF helps to promote the performance of scene parsing. Experiments on benchmark datasets show that the proposed approach outperforms the state-of-the-art approaches for scene parsing.