A Real Time Video Summarization for YouTube Videos and Evaluation of Computational Algorithms for their Time and Storage Reduction

Theaim of creating video summarization is for gathering huge video data and makes important points to be highlighted. Focus of this view is to avail the complete content of data for any particular video can be easy and clarity of indexing video. In recent days people use internet to surf and watch videos, images, play games, shows and many more activities. But it is highly impossible to go through each and every show or video because it can consume more time and data. Instead, providing highlights of any such shows or game videos then it will be helpful to go through and decide about that video. Also we can provide trailer part of any news/movie videos which can yield to make judgement of those incidents. We propose an interesting principle for highlighting videos mostly they can be online. These online videos can be shortened and summarized the huge video into smaller parts. In order to achieve this we use feature extracting algorithms called the gradient and optical flow histograms (HOG & HOF). In order to enhance the efficiency of the method several optimization techniques are also being implemented

Video summarisation: A conceptual framework and survey of the state of the art

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users

On-line video abstraction

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, abril de 201

VIDEO SUMMARIZATION BY SPATIAL-TEMPORAL GRAPH OPTIMIZATION

In this paper we present a novel approach for video summarization based on graph optimization. Our approach emphasizes both a comprehensive visual-temporal content coverage and visual coherence of the video summary. The approach has three stages. First, the source video is segmented into video shots, and a candidate shot set is selected from the video shots according to some video features. Second, a dissimilarity function is defined between the video shots to describe their spatial-temporal relation, and the candidate video shot set is modelled into a directional graph. Third, we outline a dynamic programming algorithm and use it to search the longest path in the graph as the final video skimming. A static video summary is generated at the same time. Experimental results show encouraging promises of our approach for video summarization. 1

VIDEO SUMMARIZATION BY SPATIAL-TEMPORAL GRAPH OPTIMIZATION

In this paper we present a novel approach for video summarization based on graph optimization. Our approach emphasizes both a comprehensive visual-temporal content coverage and visual coherence of the video summary. The approach has three stages. First, the source video is segmented into video shots, and a candidate shot set is selected from the video shots according to some video features. Second, a dissimilarity function is defined between the video shots to describe their spatial-temporal relation, and the candidate video shot set is modelled into a directional graph. Third, we outline a dynamic programming algorithm and use it to search the longest path in the graph as the final video skimming. A static video summary is generated at the same time. Experimental results show encouraging promises of our approach for video summarization. 1