Hierarchical video summarisation in reference frame subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently

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

DC-image for real time compressed video matching

This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity

RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis

Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content.Comment: This paper has been withdrawn by the author due to a crucial sign error in equation

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

Extraction of Significant Video Summaries by Dendrogram Analysis

In the current video analysis scenario, effective clustering of shots facilitates the access to the content and helps in understanding the associated semantics. This paper introduces a cluster analysis on 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 dendrograms, 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 by testing it on a collection of video-data from different kinds of programmes. Results are evaluated in terms of metrics that measure the content representational value of the summarization technique