An Overview of Video Shot Clustering and Summarization Techniques for Mobile Applications

The problem of content characterization of video programmes is of great interest because video appeals to large audiences and its efficient distribution over various networks should contribute to widespread usage of multimedia services. In this paper we analyze several techniques proposed in literature for content characterization of video programmes, including movies and sports, that could be helpful for mobile media consumption. In particular we focus our analysis on shot clustering methods and effective video summarization techniques since, in the current video analysis scenario, they facilitate the access to the content and help in quick understanding of the associated semantics. First we consider the shot clustering techniques based on low-level features, using visual, audio and motion information, even combined in a multi-modal fashion. Then we concentrate on summarization techniques, such as static storyboards, dynamic video skimming and the extraction of sport highlights. Discussed summarization methods can be employed in the development of tools that would be greatly useful to most mobile users: in fact these algorithms automatically shorten the original video while preserving most events by highlighting only the important content. The effectiveness of each approach has been analyzed, showing that it mainly depends on the kind of video programme it relates to, and the type of summary or highlights we are focusing on

Experimental demonstration of evanescent coupling from optical fibre tapers to photonic crystal waveguides

Experimental results demonstrating nearly complete mode-selective evanescent coupling to a photonic crystal waveguide from an optical fibre taper are presented. Codirectional coupling with 98% maximum power transfer to a photonic crystal waveguide of length 65 μm and with a coupling bandwidth of 20 nm is realised

Video Shot Clustering and Summarization through dendrograms

In the context of analysis of video documents, effective clustering of shots facilitates the access to the content and helps in understanding the associated semantics. This paper introduces a cluster analysis on video shots which employs dendrogram representation to produce hierarchical summaries of the video document. Vector quantization codebooks are used to represent the visual content and to group the shots with similar chromatic consistency. The evaluation of the cluster codebook distortions, and the exploitation of the dependency relationships on the dendrogram, allow to obtain only a few significant summaries of the whole video. Finally the user can navigate through summaries and decide which one best suites his/her needs for eventual post processing. The effectiveness of the proposed method is demonstrated, on a collection of different video programmes, in term of metrics that measure the content representational value of the summarization technique

Hierarchical Structuring of Video Previews by Leading-Cluster-Analysis

3noClustering of shots is frequently used for accessing video data and enabling quick grasping of the associated content. In this work we first group video shots by a classic hierarchical algorithm, where shot content is described by a codebook of visual words and different codebooks are compared by a suitable measure of distortion. To deal with the high number of levels in a hierarchical tree, a novel procedure of Leading-Cluster-Analysis is then proposed to extract a reduced set of hierarchically arranged previews. The depth of the obtained structure is driven both from the nature of the visual content information, and by the user needs, who can navigate the obtained video previews at various levels of representation. The effectiveness of the proposed method is demonstrated by extensive tests and comparisons carried out on a large collection of video data. of digital videos has not been accompanied by a parallel increase in its accessibility. In this context, video abstraction techniques may represent a key components of a practical video management system: indeed a condensed video may be effective for a quick browsing or retrieval tasks. A commonly accepted type of abstract for generic videos does not exist yet, and the solutions investigated so far depend usually on the nature and the genre of video data.openopenBenini, Sergio; Migliorati, Pierangelo; Leonardi, RiccardoBenini, Sergio; Migliorati, Pierangelo; Leonardi, Riccard

Using Content Analysis for Video Compression

This paper suggests the idea to model video information as a concatenation of different recurring sources. For each source a different tailored compressed representation can be optimally designed so as to best match the intrinsic characteristics of the viewed scene. Since in a video, a shot or scene with similar visual content recurs more than once, even at distant intervals in time, this enables to build a more compact representation of information. In a specific implementation of this idea, we suggest a content-based approach to structure video sequences into hierarchical summaries, and have each such summary represented by a tailored set of dictionaries of codewords. Vector quantization techniques, formerly employed for compression purposes only, have been here used first to represent the visual content of video shots and then to exploit visual-content redundancy inside the video. The depth in the hierarchy determines the precision in the representation both from a structural point of view and from a quality level in reproducing the video sequence. The effectiveness of the proposed method is demonstrated by preliminary tests performed on a limited collection of video-data excerpted from a feature movie. Some additional functionalities such as video skimming may remarkably benefit from this type of representation

Video Summarization Based on a Fuzzy Based Incremental Clustering

The significant development of multimedia and dijital video production in recent years has led to the mass production of personal and commerical video archives.Therefore, the need for efficient tools and methods of accessing video content and information rapidly is significantly increasing. Video summarization is the removal of visual redundancy and repetitive video frames,and obtaining a short summary of the whole video so that the summary obtained effectively reflects the whole video content. Examples of these summarizations in recent years include STIMO and VSUMM.According to users' comments, in the mentioned methods, the summarization has a high rate of error in a full report of summarization and a low accuracy in non-repetitive frames production, as well as a high computation time.In this paper.in order to solve these problems,we developed a system which modeled users' and supervisors' comments.We used a fuzzy based incremental clustering by which the selection and deselection of frames are done based on fuzzy rules. The extracted rules were determined based on users' comments on the video summarization.Finally, we performed our proposed method on the video clips used in the previous methodes.Produced summaries were evaluated by a qualitative method to minimize human interferences.The results obtained indicate the high accuracy of summarization and the less computation time.DOI:http://dx.doi.org/10.11591/ijece.v4i4.583