An interactive framework for soft segmentation and matting of natural images and videos is presented in this paper. The proposed technique is based on the optimal, linear time, computation of weighted geodesic distances to user-provided scribbles, from which the whole data is automatically segmented. The weights are based on spatial and/or temporal gradients, considering the statistics of the pixels scribbled by the user, without explicit optical flow or any advanced and often computationally expensive feature detectors. These could be naturally added to the proposed framework as well if desired, in the form of weights in the geodesic distances. An automatic localized refinement step follows this fast segmentation in order to further improve the results and accurately compute the corresponding matte function. Additional constraints into the distance definition permit to efficiently handle occlusions such as people or objects crossing each other in a video sequence. The presentation of the framework is complemented with numerous and diverse examples, including extraction of moving foreground from dynamic background in video, natural and 3D medical images, and comparisons with the recent literature.

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In recent years, Weibo has greatly enriched people s life. More and more people are actively sharing information with others and expressing their opinions and feelings on Weibo. Analyzing emotion hidden in this information can benefit online marketing, branding, customer relationship management and monitoring public opinions. Sentiment analysis is to identify the emotional tendencies of the microblog messages, that is to classify users' emotions into positive, negative and neutral. This paper presents a novel model to build a Sentiment Dictionary using Word2vec tool based on our Semantic Orientation Pointwise Similarity Distance (SO-SD) model. Then we use the Emotional Dictionary to obtain the emotional tendencies of Weibo messages. Through the experiment, we validate the effectiveness of our method, by which we have performed a preliminary exploration of the sentiment analysis of Chinese Weibo in this paper.