Shot Boundary Detection Based On Soft Computation

In recent years, the content-based video retrieval is becoming one of the most advanced and active research topics in the field of the information indexing in the world. Due to the foreground of wide theory and application shown in the fields of information indexing and data mining, the content-based video indexing has been highly regarded by researchers and experts all over the world. This paper focuses on the key technique—shot boundary detection. Based on the soft computation method, such as rough sets, clustering analysis and so on, some effective methods have been proposed, and the segmentation of news items has been implemented according to the several indexing clues. The main research fruits achieved in this thesis are given as follows:Chapter 1 introduces the background and significance of our work firstly. Then, a brief review of the advances and status for the content-based video indexing and shot boundary detection is presented and the research orientation of this paper is point out by analyzing the major defects in shot boundary detection.In Chapter 2, a brief introduction to the definitions and classifications of soft computations is made, with the emphasis on several soft computation methods used in this thesis. Then, the method combining with rough sets used in this dissertation to other mathematical tools or soft computation methods are discuss in detail with the comparison of their performances.Analyzing the relationship between video features, the types of shot transitions and conditional attributes of rough sets, decision attributes of rough sets, the rough sets and fuzzy clustering based method for shot boundary detection is proposed in Chapter 3 and has been successfully applied into the segmentation of cut transition, gradual transition and no transition in news video. For the drawbacks of rough sets dealing with data, the rough-fuzzy sets is introduced and the definition of rough-fuzzy operator is given. According to the feature of video in big data, the detailed procedure of"training before detection"and the automatic threshold method are brought foreword, which improves the automatization degree.Firstly, it is studied that exacting strict problems for data veracity in rough sets and the limitation of fuzzy c-modes is analyzed in Chapter 4, the variable precision rough sets is introduced and the feature weighted clustering method based on the...