This paper deals with nano minimal open sets and nano maximal open sets. Thereafter nano maximal, nano minimal open sets, nano minimal continuous and nano maximal continuous are studied. Also we study the interrelationship among these concepts. Finally we obtain a result which is not possible in classical topology.
Background: The massive database of videos is growing day by day in this era. Analyzing such huge data is always a time-consuming process. The effective use of video content requires a user-friendly access to information. This leads to the evolution of the research area known as video summarization. The effective techniques of video summarization, the videos have let to analyze the content of large volumes of digital video sequences in various categories, such as surveillance, documentaries, movies, sports, lectures, and news. In video summarization, the automatic selection of necessary and informative section from videos using accurate algorithms is essential. The keyframe extraction in video summarization is intended to suffice comprehensive analysis of video by eliminating replications and extraction of keyframes from the video. Methods: Recent keyframe extraction techniques like clustering, shot, visual content based keyframe extraction methods are discussed for effective keyframe extraction. Results: First an introduction of various techniques for keyframe extraction pursued by the state-ofthe-art review on their properties. Although we have outlined some ideas for effective evaluation of video keyframes, the analytical evaluation of various keyframe extraction techniques is discussed and the approaches based on the methods, dataset and the results are compared. Conclusion: In the recent years, the use of digital video data has been increasing significantly due to the extensive use of multimedia applications in the areas of education, entertainment, business. So the video has received an incredible attention and research interest in video processing. The use of keyframe extraction has been given incredible attention, in this work, we have carried out a comprehensive survey and review of the research in keyframe extraction techniques. We believe the review paper will provide an update for the reader regarding the progress of keyframe extraction by different keyframe extraction techniques.
The objective of this paper is to develop a video summarization system to extract significant frames of interest from a given video. To meet the objective, it is proposed to consider both the static features and the wavelet features. Visual attention integrated from the static and wavelet feature set are combined using a prioritized fusion method. Experimental results shows that the static features dominate in certain videos and wavelet features dominate in certain videos. Hence, the proposed fusion approach is suitable for slow motion videos and fast moving videos. Further, the performance of proposed work outperforms the state-of-art methods for video summarization.
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