Static Video Summaries Research Articles

Creating Video Visual Storyboard with Static Video Summarization using Fractional Energy of Orthogonal Transforms

The overwhelming number of video uploads and downloads has made it incredibly difficult to find, gather, and archive videos. A static video summarization technique highlights an original video's significant points through a set of static keyframes as a video visual storyboard. The video visual storyboards are created as static video summaries that solve video processing-related issues like storage and retrieval. In this paper, a strategy for effectively summarizing static videos using the feature vectors, which are fractional coefficients of the transformed video frames, is proposed and evaluated. Four popular orthogonal transforms are deployed for generating feature vectors of video frames. The fractional coefficients of transformed video frames taken as 25 percent, 6.25 percent, and 1.5625 percent of full 100 percent transformed coefficients are considered to form video visual storyboards. The proposed method uses the benchmark video datasets Open Video Project (OVP) and SumMe to validate the performance, containing user summaries (storyboards). These video summaries created using the proposed method are evaluated using percentage accuracy and matching rate.

Human consistency evaluation of static video summaries

Automatic video summarization aims to provide brief representation of videos. Its evaluation is quite challenging, usually relying on comparison with user summaries. This study views it in a different perspective in terms of verifying the consistency of user summaries, as the outcome of video summarization is usually judged based on them. We focus on human consistency evaluation of static video summaries in which the user summaries are evaluated among themselves using the consistency modelling method we proposed recently. The purpose of such consistency evaluation is to check whether the users agree among themselves. The evaluation is performed on different publicly available datasets. Another contribution lies in the creation of static video summaries from the available video skims of the SumMe datatset. The results show that the level of agreement varies significantly between the users for the selection of key frames, which denotes the hidden challenge in automatic video summary evaluation. Moreover, the maximum agreement level of the users for a certain dataset, may indicate the best performance that the automatic video summarization techniques can achieve using that dataset.

VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method

The fast evolution of digital video has brought many new multimedia applications and, as a consequence, has increased the amount of research into new technologies that aim at improving the effectiveness and efficiency of video acquisition, archiving, cataloging and indexing, as well as increasing the usability of stored videos. Among possible research areas, video summarization is an important topic that potentially enables faster browsing of large video collections and also more efficient content indexing and access. Essentially, this research area consists of automatically generating a short summary of a video, which can either be a static summary or a dynamic summary. In this paper, we present VSUMM, a methodology for the production of static video summaries. The method is based on color feature extraction from video frames and k-means clustering algorithm. As an additional contribution, we also develop a novel approach for the evaluation of video static summaries. In this evaluation methodology, video summaries are manually created by users. Then, several user-created summaries are compared both to our approach and also to a number of different techniques in the literature. Experimental results show – with a confidence level of 98% – that the proposed solution provided static video summaries with superior quality relative to the approaches to which it was compared.

Video collage: presenting a video sequence using a single image

The explosive growth of video data demands the video presentation technique which supports fast browsing of video content. In this paper, we present an automatic procedure for constructing a compact synthesized collage from a video sequence. The synthesized image, called “Video Collage”, is a kind of static video summary—to select the most representative images from video, to extract salient regions of interest (ROIs) from these images, and to seamlessly arrange ROIs on a given canvas with the temporal structure of video content preserved. We formulate the generation of Video Collage as a unified energy minimization problem in which each of above desirability is represented by an energy term. We start from the basic setting of Video Collage in which both the shape of ROIs and collage are fixed as rectangular, and then show how it can support arbitrary shapes of ROIs, as well as a variety of collage templates and region of interest (ROI) arrangement layouts (i.e., book, diagonal, and spiral). The experiments show its effectiveness to present a video in a very compact and visually appealing form while preserving the necessary information to understand the video.