In order to overcome the initial seed point selection and less robust of the order growth in the general region growing algorithm, the color image region growing algorithm is proposed with a robust order growth in this study. First, the local color histograms of all the pixels and Neighbor Similarity Factor (NSF) are calculated. Secondly, the seed selection rules, seed growth criteria and growth termination criterion are established by NSF value, segmentation is early made for image. Finally, unclassified points are reclassified to get the final segmentation result. By comparison with the JSEG algorithm testing, computation time and accuracy of the segmentation algorithm has obvious advantages.

In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.

Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.

The Synchronized Multimedia Integration Language (SMIL) allows Web designers to design complicated and vivid multimedia presentations in a declarative manner. In this paper, an abstract semantic model called Multimedia Augmented Transition Network (MATN) is used to model the conceptual structure of SMIL, as well as the temporal relations and synchronization control. The advantages of using the MATN model are its simplicity and ease of modification (scalability). Users can easily generate their favorite multimedia presentations using MATNs and render them using the SMIL players. Since the SMIL specification is quite new to the Internet society, the functionality of the players is limited, i.e., they often acquire proprietary platforms or cannot deal with MPEG or RTP media objects. Another contribution of this paper is to propose a novel architecture based on Java JMF technology for tackling with such constraints.