Performance Evaluation of Video Summaries Using Efficient Image Euclidean Distance

Sivapriyaa Kannappan, Yonghuai Liu, Bernard Tiddeman

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (SciVal)


Video summarization aims to manage video data by providing succinct representation of videos, however its evaluation is somewhat challenging. IMage Euclidean Distance (IMED) has been proposed for the measurement of the similarity of two images. Though it is effective and can tolerate the distortion and/or small movement of the objects, its computational complexity is high in the order of O(n2)O(n2). This paper proposes an efficient method for evaluating the video summaries. It retrieves a set of matched frames between automatic summary and the ground truth summary through two way search, in which the similarity between two frames are measured using the Efficient IMED (EIMED), which considers neighboring pixels, rather than all the pixels in the frames. Experimental results based on a publicly accessible dataset has shown that the proposed method is effective in finding precise matches and usually discards the false ones, leading to a more objective measurement of the performance for various techniques
Original languageEnglish
Title of host publicationAdvances in Visual Computing
Subtitle of host publicationProceedings 12th International Symposium, ISVC 2016
EditorsGeorge Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Fatih Porikli, Sandra Skaff, Alireza Entezari, Jianyuan Min, Danisuke Iwai, Amela Sadagic, Carlos Scheidegger, Tabias Isenberg
Place of PublicationLas Vegas, USA
PublisherSpringer Nature
Number of pages9
EditionPart II
ISBN (Electronic)978-3-319-50832-0
ISBN (Print)978-3-319-50831-3, 3319508318
Publication statusPublished - 10 Dec 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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