Personalized video summarization based on group scoring

In this paper an expert-based model for generation of personalized video summaries is suggested. The video frames are initially scored and annotated by multiple video experts. Thereafter, the scores for the video segments that have been assigned the higher priorities by end users will be upgraded. Considering the required summary length, the highest scored video frames will be inserted into a personalized final summary. For evaluation purposes, the video summaries generated by our system have been compared against the results from a number of automatic and semi-automatic summarization tools that use different modalities for abstraction

A user-centered approach to rushes summarisation via highlight-detected keyframes

We present our keyframe-based summary approach for BBC Rushes video as part of the TRECVid Summarisation benchmark evaluation carried out in 2007. We outline our approach to summarisation that uses video processing for feature extraction and is informed by human factors considerations for summary presentation. Based on the performance of our generated summaries as reported by NIST, we subsequently undertook detailed failure analysis of our approach. The findings of this investigation as well as recommendations for alterations to our keyframe-based summary generation method, and the evaluation methodology for Rushes summaries in general, are detailed within this paper

The TRECVID 2007 BBC rushes summarization evaluation pilot

This paper provides an overview of a pilot evaluation of video summaries using rushes from several BBC dramatic series. It was carried out under the auspices of TRECVID. Twenty-two research teams submitted video summaries of up to 4% duration, of 42 individual rushes video files aimed at compressing out redundant and insignificant material. The output of two baseline systems built on straightforward content reduction techniques was contributed by Carnegie Mellon University as a control. Procedures for developing ground truth lists of important segments from each video were developed at Dublin City University and applied to the BBC video. At NIST each summary was judged by three humans with respect to how much of the ground truth was included, how easy the summary was to understand, and how much repeated material the summary contained. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 78% and results indicate that while it is difficult to exceed the performance of baselines, a few systems did

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method

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 Video Summaries by Dendrogram Cluster Analysis

In the current video analysis scenario, effective summarization of video sequences through shot clustering facilitates the access to the content and helps in understanding the associated semantics. This paper introduces a generic scheme to produce hierarchical summaries of the video document starting from a dendrogram representation of clusters of shots. The evaluation of the cluster distortions, and the exploitation of the dependency relationships between clusters on the dendrograms, allow to obtain only a few semantically 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, using and comparing different visual features on color information. Results are evaluated in terms 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