Abstract
Similarity measures are very crucial especially in the field of information retrieval. Thus, various distance/similarity measures were proposed throughout the literature. In the video retrieval field, videos are represented as multi-dimensional features vector. Once this features vector is extracted from video shots; the retrieval task is primarily performed based on the measurement of similarity between respective videos’ feature vectors. Moreover, the retrieval quality could be greatly improved with careful distance measure selection. This paper presents an extensive analysis regarding the most commonly used video retrieval similarity measures. The results are consolidated with a multifaceted analysis, i.e. multiple challenging video datasets, retrieval curves and confusion matrices. The major contribution of this paper is investigating the effectiveness of the common similarity measures from a video retrieval perspective. This would give the field researchers the required knowledge to select the most suitable distance measure for their video retrieval research work.
Similar content being viewed by others
Notes
The terms similairty measure and distance metric are used interchangibly in this paper.
Average±Standard deviation.
References
Altadmri A, Ahmed A (2014) A framework for automatic semantic video annotation. Multimed Tools Appl 72(2):1167–1191
Basharat A, Zhai Y, Shah M (2008) Content based video matching using spatiotemporal volumes. Comput Vis Image Underst 110(3):360–377
Bekhet S, Ahmed A (2017) Video similarity detection using fixed-length statistical dominant colour profile (SDCP) signatures. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-017-0700-9
Bekhet S, Ahmed A, Hunter A (2014) Dc-image for real time compressed video matching. In: Transactions on engineering technologies. Springer, pp 513–527
Bekhet S, Ahmed A, Altadmri A, Hunter A (2016) Compressed video matching: Frame-to-frame revisited. Multimed Tools Appl 75(23):15,763–15,778
Bekhet S, Ahmed A (2018) Graph-based video sequence matching using dominant colour graph profile (DCGP). SIViP 12(2):291–298. https://doi.org/10.1007/s11760-017-1157-9
Bekhet S, Ahmed A (2018) An integrated signature-based framework for efficient visual similarity detectionan integrated signature-based framework for efficient visual similarity detection and measurement in video shots. ACM Trans Inf Syst (TOIS) 36(4):37
Black PE (2004) Dictionary of algorithms and data structures. National Institute of Standards and Technology
Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. City 1(2):1
Chardy P, Glemarec M, Laurec A (1976) Application of inertia methods to benthic marine ecology: practical implications of the basic options. Estuarine Coast Mar Sci 4(2):179–205
Dubuisson S (2010) The computation of the bhattacharyya distance between histograms without histograms. In: 2010 2nd international conference on Image processing theory tools and applications (IPTA). IEEE, pp 373–378
Jiang L, Li C (2019) Two improved attribute weighting schemes for value difference metric. Knowledge and information systems 60(2):949–970
Kantorov V, Laptev I (2014) Efficient feature extraction, encoding, and classification for action recognition. In: 2014 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp 2593–2600
Krause EF (1975) Taxicab geometry: An adventure in non-Euclidean geometry. Courier Corporation
Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos ”in the wild”. In: 2009. CVPR 2009. IEEE conference on Computer vision and pattern recognition. IEEE, pp 1996–2003
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Manning CD, Raghavan P, Schütze H et al (2008) Introduction to information retrieval, vol 1. Cambridge University Press, Cambridge
Ng CW, King I, Lyu MR (2001) Video comparison using tree matching algorithms. In: Proceedings of The International Conference on Imaging Science, Systems, and Technology, vol 1, pp 184–190
Rodriguez MD, Ahmed J, Shah M (2008) Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: 2008. CVPR 2008. IEEE conference on Computer vision and pattern recognition, pp 1–8
Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40(2):99–121
Sadanand S, Corso JJ (2012) Action bank: a high-level representation of activity in video. In: 2012 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1234–1241
Sathya N, Rathi S (2018) A survey on reducing the semantic gap in content based image retrieval system. Int J Adv Stud Comput Sci Eng 7(3):9–17
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32
TrecVid(2011): Trec video retrival task, bbc ruch (1-02-2011) (2011). www.nplpir.nist.gov/projects/trecvid
Van Der Heijden F, Duin RP, De Ridder D, Tax DM (2005) Classification, parameter estimation and state estimation: an engineering approach using MATLAB. Wiley, New York
Yang L, Jin R (2006) Distance metric learning: A comprehensive survey Michigan State Universiy 2(2):4
Yang L, Jin R, Mummert L, Sukthankar R, Goode A, Zheng B, Hoi SC, Satyanarayanan M (2010) A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE Trans Pattern Anal Mach Intell 32(1):30–44
YouTube: Youtube statistics (2014). http://www.youtube.com/yt/press/statistics.html
Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024
Zhang D, Lu G (2003) Evaluation of similarity measurement for image retrieval. In: 2003. Proceedings of the 2003 International Conference on Neural Networks and Signal Processing. IEEE, vol 2, pp 928–931
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bekhet, S., Ahmed, A. Evaluation of similarity measures for video retrieval. Multimed Tools Appl 79, 6265–6278 (2020). https://doi.org/10.1007/s11042-019-08539-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08539-4