This paper investigates a general framework to discover categories of unlabeled scene images according to their appearances (i.e., textures and structures). We jointly solve the two coupled tasks in an unsupervised manner: 1) classifying images without predetermining the number of categories and 2) pursuing generative model for each category. In our method, each image is represented by two types of image descriptors that are effective to capture image appearances from different aspects. By treating each image as a graph vertex, we build up a graph and pose the image categorization as a graph partition process. Specifically, a partitioned subgraph can be regarded as a category of scenes and we define the probabilistic model of graph partition by accumulating the generative models of all separated categories. For efficient inference with the graph, we employ a stochastic cluster sampling algorithm, which is designed based on the Metropolis-Hasting mechanism. During the iterations of inference, the model of each category is analytically updated by a generative learning algorithm. In the experiments, our approach is validated on several challenging databases, and it outperforms other popular state-of-the-art methods. The implementation details and empirical analysis are presented as well.

Video shots are often treated as the basic elements for retrieving information from videos. In recent years, video shot categorization has received increasing attention, but most of the methods involve a procedure of supervised learning, i.e., training a multi-class predictor (classifier) on the labeled data. In this paper, we study a general framework to unsupervisedly discover video shot categories. The contributions are three-fold in feature, representation, and inference: (1) A new feature is proposed to capture local information in videos, defined with small video patches (e.g.,$11 \times 11 \times 5$pixels). A dictionary of video words can be thus clustered off-line, characterizing both appearance and motion dynamics. (2) We pose the problem of categorization as an automated graph partition task, in that each graph vertex represents a video shot, and a partitioned sub-graph consisting of connected graph vertices represents a clustered category. The model of each video shot category can be analytically calculated by a projection pursuit type of learning process. (3) An MCMC-based cluster sampling algorithm, namely Swendsen-Wang cuts, is adopted to efficiently solve the graph partition. Unlike traditional graph partition techniques, this algorithm is able to explore the nearly global optimal solution and eliminate the need for good initialization. We apply our method on a wide variety of 1600 video shots collected from Internet as well as a subset of TRECVID 2010 data, and two benchmark metrics, i.e., Purity and Conditional Entropy, are adopted for evaluating performance. The experimental results demonstrate superior performance of our method over other popular state-of-the-art methods.

Scanned halftone images are degraded for the presence of screen patterns. It's a challenge to automatically detect the halftone images and remove the noises on the fly. This paper proposes a novel adaptive real-time descreening method based on Support Vector Machine (SVM) and modified Smoothing over Univalue Segment Assimilating Nucleus (SUSAN) filter for imaging devices, including scanners and multifunction printers. The proposed algorithm contains two major steps: image classification and adaptive descreening. The image classification uses SVM methods to accurately classify the scanned images into three categories: continuous tone, amplitude modulation (AM) halftone or frequency modulation (FM) halftone. The halftone images are needed to descreen. The proposed descreening method is based on modified SUSAN filter. It considers screen cell size to choose the optimal filter parameters which can preserve more high frequency image detail. The experiment results show that the algorithm is effective and fully automatism, and maintains higher image quality.