Abstract
Content Based Video Retrieval (CBVR) is so popular these days, because of the increased utilization of video based analytical systems. Video based analytics is quite effective than image analysis, as a series of actions are captured by the video. This ends up with better decision making ability. The CBVR systems play an important role in boosting the human–computer interaction. This paper presents a multimodal CBVR that takes both the visual and audio information into account for retrieving relevant videos to the user. Two modules are employed by this work to deal with video and audio data. The video data is processed to detect the significant frame from shots and is achieved by Lion Optimization Algorithm (LOA). The features are extracted from the visual data and with respect to the audio data, MHEC and LPCC features are extracted. The extracted features are clustered by Kernelized Fuzzy C Mean (KFCM) algorithm. Finally, the feature database is formed and is utilized in the query matching process during the testing phase. The performance of the proposed work is tested in terms of precision, recall, F-measure and time consumption rates. The proposed CBVR system proves better performance than the existing approaches and is evident through attained results.
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06 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04085-4
References
Antoniol G, Rollo VF, Venturi G (2005) Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories. In: Proceedings of the 2005 international workshop on mining software repositories
Araujo A, Girod B (2017) Large-scale video retrieval using image queries. IEEE Trans Circuits Syst Video Technol 28(6):1406–1420
Baroffio L, Canclini A, Cesana M, Redondi A, Tagliasacchi M, Tubaro S (2015) Coding local and global binary visual features extracted from video sequences. IEEE Trans Image Process 24(11):3546–3560
Bi C, Yuan Y, Zhang J, Shi Y, Xiang Y, Wang Y, Zhang R (2018) Dynamic mode decomposition based video shot detection. IEEE Access 6:21397–21407
Castañón G, Elgharib M, Saligrama V, Jodoin PM (2015) Retrieval in long-surveillance videos using user-described motion and object attributes. IEEE Trans Circuits Syst Video Technol 26(12):2313–2327
Chiu CY, Chao SP, Wu MY, Yang SN, Lin HC (2004) Content-based retrieval for human motion data. J Vis Commun Image Represent 15(3):446–466
Chou CL, Chen HT, Lee SY (2015) Pattern-based near-duplicate video retrieval and localization on web-scale videos. IEEE Trans Multimed 17(3):382–395
Dong J, Li X, Snoek CG (2018) Predicting visual features from text for image and video caption retrieval. IEEE Trans Multimed 20(12):3377–3388
Feng Y, Zhou P, Xu J, Ji S, Wu DO (2018) Video big data retrieval over media cloud: a context-aware online learning approach. IEEE Trans Multimed 21(7):1762–1777. https://doi.org/10.1109/TMM.2018.2885237
Fernandes FC, van Spaendonck RL, Burrus CS (2004) Multidimensional, mapping-based complex wavelet transforms. IEEE Trans Image Process 14(1):110–124
Git clone. https://github.com/gtoderici/sports-1m-dataset.git. Accessed 10 Feb 2020
Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):797–819
Jawahar CV, Chennupati B, Paluri B, Jammalamadaka N (2005) Video retrieval based on textual queries. In: Proceedings of the thirteenth international conference on advanced computing and communications, Coimbatore, pp 1–6
Kekre HB, Mishra D, Rege MP (2015) Survey on recent techniques in content based video retrieval. Int J Eng Tech Res IJETR 3(5):69–73
Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–498
Kurzhals K, John M, Heimerl F, Kuznecov P, Weiskopf D (2016) Visual movie analytics. IEEE Trans Multimed 18(11):2149–2160
Li C, Zhou B (2020) Fast key-frame image retrieval of intelligent city security video based on deep feature coding in high concurrent network environment. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01679-8
Li K, Li S, Oh S, Fu Y (2017) Videography-based unconstrained video analysis. IEEE Trans Image Process 26(5):2261–2273
Lokoc J, Bailer W, Schoeffmann K, Muenzer B, Awad G (2018) On influential trends in interactive video retrieval: video browser showdown 2015–2017. IEEE Trans Multimed 20(12):3361–3376
Marques O, Furht B (2002) Content-based image and video retrieval, vol 21. Springer Science & Business Media, New York
Mccomb K et al (1993) Female lions can identify potentially infanticidal males from their roars. Proc R Soc Lond Ser B Biol Sci 252(1333):59–64
Sadjadi SO, Hasan T, Hansen JHL (2012) Mean Hilbert Envelope Coefficients (MHEC) for robust speaker recognition. In: Interspeech, pp 1696–1699
Selesnick IW (2006) A higher density discrete wavelet transform. IEEE Trans Signal Process 54(8):3039–3048
Shen RK, Lin YN, Juang TTY, Shen VR, Lim SY (2017) Automatic Detection of video shot boundary in social media using a hybrid approach of HLFPN and keypoint matching. IEEE Trans Comput Soc Syst 5(1):210–219
Thomas SS, Gupta S, Subramanian VK (2018) Context driven optimized perceptual video summarization and retrieval. In: IEEE transactions on circuits and systems for video technology
Veltkamp RC, Burkhardt H, Kriegel HP (eds) (2013) State-of-the-art in content-based image and video retrieval, vol 22. Springer Science & Business Media, New York
Wang Y, Zhang Y, Sheng M, Guo K (2019) On the interaction of video caching and retrieving in multi-server mobile-edge computing systems. IEEE Wirel Commun Lett 8(5):1444–1447
Yang H, Meinel C (2014) Content based lecture video retrieval using speech and video text information. IEEE Trans Learn Technol 7(2):142–154
Yao J, Zhang YT (2001) Bionic wavelet transform: a new time–frequency method based on an auditory model. IEEE Trans Biomed Eng 48(8):856–863
Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3:24–36
Zhang X, Ma S, Wang S, Zhang X, Sun H, Gao W (2016) A joint compression scheme of video feature descriptors and visual content. IEEE Trans Image Process 26(2):633–647
Zhang P, Thomas T, Zhuo T, Huang W, Huang H (2017) Object coding based video authentication for privacy protection in immersive communication. J Ambient Intell Humaniz Comput 8(6):871–884
Zhenjiang M (2000) Zernike moment—based image shape analysis and its application. Pattern Recogn Lett 21:169–177
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04085-4"
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Prathiba, T., Kumari, R.S.S. RETRACTED ARTICLE: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction. J Ambient Intell Human Comput 12, 6215–6229 (2021). https://doi.org/10.1007/s12652-020-02190-w
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DOI: https://doi.org/10.1007/s12652-020-02190-w