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Static video summarization with multi-objective constrained optimization

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Abstract

Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods.

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Data availability

The datasets analyzed during the current study are available in the following link. https://sites.google.com/site/vsummsite/download.

References

  • Angadi S, Naik V (2014) Entropy based fuzzy c means clustering and key frame extraction for sports video summarization. In: 2014 Fifth International Conference on signal and image processing, pp 271–279, https://doi.org/10.1109/ICSIP.2014.49

  • Ankerst M, Breunig MM, Kriegel HP et al (1999) Optics: ordering points to identify the clustering structure. ACM SIGMOD Rec 28(2):49–60

    Article  Google Scholar 

  • Asim M, Almaadeed N, Al-Máadeed S, et al (2018) A key frame based video summarization using color features. In: 2018 Colour and Visual Computing Symposium (CVCS), IEEE, pp 1–6

  • Basavarajaiah M, Sharma P (2021) Gvsum: generic video summarization using deep visual features. Multimedia Tools Appl 80:14459–14476

    Article  Google Scholar 

  • Belo L, Caetano C, Patrocinio Z, et al (2014) Graph-based hierarchical video summarization using global descriptors. In: 2014 IEEE 26th International Conference on tools with artificial intelligence, IEEE, pp 822–829

  • Bendraou Y, Essannouni F, Salam A (2019) From local to global key-frame extraction based on important scenes using SVD of centrist features. Multimedia Tools Appl 78:1441–1456

    Article  Google Scholar 

  • Chai C, Lu G, Wang R et al (2021) Graph-based structural difference analysis for video summarization. Inf Sci 577:483–509

    Article  MathSciNet  Google Scholar 

  • Chang X, Ren P, Xu P et al (2021) A comprehensive survey of scene graphs: generation and application. IEEE Trans Pattern Anal Mach Intell 45(1):1–26

    Article  Google Scholar 

  • De Avila SEF, Lopes APB, da Luz A Jr et al (2011) Vsumm: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn Lett 32(1):56–68

    Article  Google Scholar 

  • Dhanushree M, Priya R, Aruna P, et al (2023) A keyframe extraction using hdbscan with particle swarm optimization. In: 2023 10th International Conference on signal processing and integrated networks (SPIN), IEEE, pp 445–450

  • Ester M, Kriegel HP, Sander J, et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd, pp 226–231

  • Fei M, Jiang W, Mao W (2017) Memorable and rich video summarization. J Vis Commun Image Represent 42:207–217

    Article  Google Scholar 

  • Furini M, Geraci F, Montangero M et al (2010) Stimo: still and moving video storyboard for the web scenario. Multimedia Tools Appl 46:47–69

    Article  Google Scholar 

  • Gharbi H, Bahroun S, Massaoudi M et al (2017) Key frames extraction using graph modularity clustering for efficient video summarization. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1502–1506

  • Gharbi H, Bahroun S, Zagrouba E (2019) Key frame extraction for video summarization using local description and repeatability graph clustering. SIViP 13:507–515

    Article  Google Scholar 

  • Guan G, Wang Z, Lu S et al (2012) Keypoint based keyframe selection. IEEE Trans Circuits Syst Video Technol 23(4):729–734

    Article  Google Scholar 

  • Gunantara N (2018) A review of multi-objective optimization: methods and its applications. Cogent Eng 5(1):1502242

    Article  Google Scholar 

  • Hannane R, Elboushaki A, Afdel K (2016a) Efficient video summarization based on motion sift-distribution histogram. In: 2016 13th international conference on computer graphics, imaging and visualization (CGiV). IEEE, pp 312–317

  • Hannane R, Elboushaki A, Afdel K et al (2016b) An efficient method for video shot boundary detection and keyframe extraction using sift-point distribution histogram. Int J Multimedia Inf Retr 5:89–104

    Article  Google Scholar 

  • Hannane R, Elboushaki A, Afdel K (2018) Mskvs: adaptive mean shift-based keyframe extraction for video summarization and a new objective verification approach. J Vis Commun Image Represent 55:179–200

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Article  Google Scholar 

  • Hu W, Xie N, Li L et al (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C (Applications and Reviews) 41(6):797–819

    Article  Google Scholar 

  • Issa O, Shanableh T (2022) Cnn and hevc video coding features for static video summarization. IEEE Access 10:72080–72091

    Article  Google Scholar 

  • Issa O, Shanableh T (2023) Static video summarization using video coding features with frame-level temporal subsampling and deep learning. Appl Sci 13(10):6065

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on neural networks, IEEE, pp 1942–1948

  • Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  • Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  Google Scholar 

  • Li J, Yao T, Ling Q et al (2017) Detecting shot boundary with sparse coding for video summarization. Neurocomputing 266:66–78

    Article  Google Scholar 

  • Li Z, Nie F, Chang X et al (2018a) Rank-constrained spectral clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 29(12):6073–6082

    Article  MathSciNet  Google Scholar 

  • Li Z, Nie F, Chang X et al (2018b) Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst 29(12):6323–6332

    Article  MathSciNet  Google Scholar 

  • Li M, Huang PY, Chang X et al (2022) Video pivoting unsupervised multi-modal machine translation. IEEE Trans Pattern Anal Mach Intell 45(3):3918–3932

    Google Scholar 

  • Martins GB, Pereira DR, Almeida JG et al (2020) Opfsumm: on the video summarization using optimum-path forest. Multimedia Tools Appl 79:11195–11211

    Article  Google Scholar 

  • Medentzidou P, Kotropoulos C (2015) Video summarization based on shot boundary detection with penalized contrasts. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE, pp 199–203

  • Mohan J, Nair MS (2019a) Domain independent redundancy elimination based on flow vectors for static video summarization. Heliyon 5(10):e02699

    Article  Google Scholar 

  • Mohan J, Nair MS (2019b) Static video summarization using sparse autoencoders. In: 2019 IEEE International Conference on electrical, computer and communication technologies (ICECCT), IEEE, pp 1–8

  • Nair MS, Mohan J (2021) Static video summarization using multi-cnn with sparse autoencoder and random forest classifier. SIViP 15:735–742

    Article  Google Scholar 

  • Parihar AS, Pal J, Sharma I (2021) Multiview video summarization using video partitioning and clustering. J Vis Commun Image Represent 74:102991

    Article  Google Scholar 

  • Park J, Lee J, Kim IJ, et al (2020) Sumgraph: video summarization via recursive graph modeling. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16, Springer, pp 647–663

  • Pramanik A, Pal SK, Maiti J et al (2022) Traffic anomaly detection and video summarization using spatio-temporal rough fuzzy granulation with z-numbers. IEEE Trans Intell Transp Syst 23(12):24116–24125

    Article  Google Scholar 

  • Rani S, Kumar M (2020) Social media video summarization using multi-visual features and Kohnen’s self organizing map. Inform Process Manag 57(3):102190

    Article  Google Scholar 

  • Sreeja M, Kovoor BC (2022) A multi-stage deep adversarial network for video summarization with knowledge distillation. J Ambient Intell Human Comput 14(8):1–16

    Google Scholar 

  • Sun Y, Li P, Jiang Z et al (2021) Feature fusion and clustering for key frame extraction. Math Biosci Eng 18(6):9294–9311

    Article  Google Scholar 

  • Thomas SS, Gupta S, Subramanian VK (2017) Event detection on roads using perceptual video summarization. IEEE Trans Intell Transp Syst 19(9):2944–2954

    Article  Google Scholar 

  • Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (aaa) for nonlinear global optimization. Appl Soft Comput 31:153–171

    Article  Google Scholar 

  • Yan C, Chang X, Li Z et al (2021) Zeronas: differentiable generative adversarial networks search for zero-shot learning. IEEE Trans Pattern Anal Mach Intell 44(12):9733–9740

    Article  Google Scholar 

  • Yasmin G, Chowdhury S, Nayak J et al (2023) Key moment extraction for designing an agglomerative clustering algorithm-based video summarization framework. Neural Comput Appl 35(7):4881–4902

    Article  Google Scholar 

  • Zhang L, Chang X, Liu J et al (2022) Tn-zstad: transferable network for zero-shot temporal activity detection. IEEE Trans Pattern Anal Mach Intell 45(3):3848–3861

    Google Scholar 

Download references

Funding

This study was funded by Ministry of Social Justice and Empowerment of the Government of India.

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Correspondence to M. Dhanushree.

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Dhanushree, M., Priya, R., Aruna, P. et al. Static video summarization with multi-objective constrained optimization. J Ambient Intell Human Comput 15, 2621–2639 (2024). https://doi.org/10.1007/s12652-024-04777-z

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