@inbook{da284b11b33b4fbc965b8c36e198d19d,
title = "Performance Evaluation of Video Summaries Using Efficient Image Euclidean Distance",
abstract = "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",
author = "Sivapriyaa Kannappan and Yonghuai Liu and Bernard Tiddeman",
year = "2016",
month = dec,
day = "10",
doi = "10.1007/978-3-319-50832-0_4",
language = "English",
isbn = "978-3-319-50831-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "33--42",
editor = "Bebis, {George } and Richard Boyle and Bahram Parvin and Darko Koracin and Fatih Porikli and Sandra Skaff and Alireza Entezari and Jianyuan Min and Danisuke Iwai and Amela Sadagic and Carlos Scheidegger and Tabias Isenberg",
booktitle = "Advances in Visual Computing",
address = "Switzerland",
edition = "Part II",
}