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During video editing, the shots composing the video are coalesced together by different types of transition effects. These editing effects are classified into abrupt and gradual changes, based on the inherent nature of these transitions. In abrupt transitions, there is an instantaneous change in the visual content of two consecutive frames. Gradual transitions are characterized by a slow and continuous change in the visual contents occurring between two shots. In this chapter, the challenges faced in this field along with an overview of the different approaches are presented. Also, a novel method for detection of dissolve transitions using a two-phased approach is enumerated. The first phase deals with detection of candidate dissolves by identifying parabolic patterns in the mean fuzzy entropy of the frames. In the second phase, an ensemble of four parameters is used to design a filter which eliminates candidates based on thresholds set for each of the four stages of filtration. The experimental results show a marked improvement over other existing methods.

This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated according to the conventional TF-IDF factor. The proposed method is found to be able to cluster a given set of text documents into a number of classes depending on their contents where the number of classes is not known a priori. It is revealed from experimental results that the proposed method of text categorization using correlation coefficient performs better than some of the other text categorization methods, including methods that use artificial neural network.

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