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Colour and orientation of pixel based video retrieval using IHBM similarity measure

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Abstract

Content-based video retrieval (CBVR) is the most energetic and stimulating research area since the early twentieth century in the domain of multimedia technology and immense quantity of retrieval techniques are introduced frequently. However, majority of the existent CBVR systems do not always give accurate results for all kinds of video databases with different colour, shape and texture feature descriptors. Sometimes, the images or videos that look similar are not semantically similar. Consequently, the retrieval outcomes that are solely centred on low level feature extraction are chiefly unsatisfactory and also unpredictable. This unlocks a new era for the research community to deviate the existent methodologies to new paradigm or direction that there is something at the back of the visual features which require to be regarded for precise searching and also retrieval. A novel CBVR methodology is suggested here centred on the selection of edge gradient feature descriptors known as HOG (Histograms of Oriented Gradients). HOG computes the edge gradient of the whole image, determines the orientation of every pixel and generates the histograms. Formerly, these extracted relevant histograms are utilized to retrieve the pertinent video frame as of the video sequence database through Integrated Histogram Bin Matching (IHBM) similarity measure. The Experimental Result of the proposed approach showed that the number of relevant retrieved video data samples is higher when compared to the existing HI Based CBVR system. The F1-score value is also high which in turn infers that the proposed approach’s performance is better when matched other existing approaches.

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References

  1. Anupriya K, Gayathri R, Balaanand M, Sivaparthipan CB (2018) Eshopping scam identification using machine learning. 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India:1–7. https://doi.org/10.1109/ICSNS.2018.8573687

  2. Asha S, Sreeraj M (2013) ‘Content based video retrieval using SURF descriptor’, IEEE Third International Conference on Advances in Computing and Communications, pp. 212–215

  3. Babu RV, Ramakrishnan KR (2007) ‘Compressed domain video retrieval using object and global motion descriptors’, Multimedia Tools and Applications, Springer, vol. 32, no. 1, pp. 93–113.

  4. BalaAnand M, Karthikeyan N, Karthik S, Sivaparthipan CB (2017) “A survey on BigData with various V's on comparison of apache hadoop and apache spark” - Advances in Natural and Applied Sciences

  5. BalaAnand M, Karthikeyan N, Karthik S (2018a) Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog. https://doi.org/10.1007/s10766-018-0598-2

  6. BalaAnand M, Karthikeyan N, Karthick S, Sivaparthipan CB (2018b) Demonetization: a visual exploration and pattern identification of people opinion on tweets. 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India:1–7. https://doi.org/10.1109/ICSNS.2018.8573616

  7. M. BalaAnand, S. Sankari, R. Sowmipriya, S. Sivaranjani "Identifying Fake User’s in Social Networks Using Non Verbal Behavior", International Journal of Technology and Engineering System (IJTES), Vol.7(2), pg:157–161.

  8. Barhoumi W et al (2013) On-the-fly extraction of key frames for efficient video summarization. AASRI Conference on Intelligent Systems and Control, Science Direct 4:78–84

    Google Scholar 

  9. Brindha N, Visalakshi P (2017) Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier. Sādhanā 42(1):1–10

    Article  MathSciNet  Google Scholar 

  10. Burkard RE, Rudolf BKR (1996) Perspective of Monge properties in optimization. Discret Appl Math 70:95–161

    Article  MathSciNet  Google Scholar 

  11. Chattopadhyay C, Das S (2013) ‘STAR: a content based video retrieval system for moving camera video shots’, IEEE Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, pp. 1–4

  12. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE international computer society conference on computer vision and pattern recognition 2:886–893

    Google Scholar 

  13. Deng Q, Wu S, Wen J, Xu Y (2018) Multi-level image representation for large-scale image-based instance retrieval. CAAI Transactions on Intelligence Technology 3(1):33–39

    Article  Google Scholar 

  14. Erol B, Kossentini F (2005) Shape-based retrieval of video objects. IEEE Transactions on Multimedia 7:179–182

    Article  Google Scholar 

  15. Hu W, Xie N, Zeng X, Maybank S (2014) A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics 41(6):797–819

    Google Scholar 

  16. Li J, Wang JZ, Wiederhold G (2000) ‘IRM: integrated region matching for image retrieval’, In Proceedings of the Eighth ACM Multimedia Conference, pp. 147–156

  17. Liang B, Xiao W, Liu X (2012) ‘Design of video retrieval system using MPEG-7 descriptors’, International Workshop on Information and Electronics Engineering, Springer, vol. 29, 2012, pp. 2578–2582

  18. Lowe D (2004), ‘Distinctive image features from scale-invariant key points’, International Journal of Computer Vision, springer, vol. 60, pp. 91–110, 2004.

  19. Maram B, Gnanasekar JM, Manogaran G et al (2018) SOCA. https://doi.org/10.1007/s11761-018-0249-x

  20. Oyama T, Yamanaka T (2018) Influence of image classification accuracy on saliency map estimation. CAAI Transactions on Intelligence Technology 3(3):140–152

    Article  Google Scholar 

  21. Patel BV, Deorankar AV, Meshram BB (2010) Content based video retrieval using entropy, edge detection, black and white color features. IEEE 2nd International Conference on Computer Engineering and Technology 6:272–276

    Google Scholar 

  22. Qi G, Zhang Q, Zeng F, Wang J, Zhu Z (2018) Multi-focus image fusion via morphological similarity-based dictionary construction and sparse representation. CAAI Transactions on Intelligence Technology 3(2):83–94

    Article  Google Scholar 

  23. Luca Rossetto et.al. (2015) ‘IMOTION — a content-based video retrieval engine’, Multimedia Modeling, Springer, Vol. 8936, pp. 255–260.

  24. 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

    Article  Google Scholar 

  25. Sivaparthipan CB, Karthikeyan N, Karthik S (2018) Designing statistical assessment healthcare information system for diabetics analysis using big data” Multimedia tools and applications

  26. Sivic J, Zisserman A (2006) ‘Video Google: efficient visual search of video’, in toward Category-Level Object Recognition, sSpringer, pp. 127–1446.s

  27. Suard F, Rakotomamonjy A, Bensrhair A (2006) ‘Pedestrian detection using infrared images and histograms of oriented gradients’, IEEE Intelligent Vehicles Symposium, pp. 206–212, Tokyo, Japan

  28. Thanga Ramya S, Arunagiri B, Rangarajan P (2017) “Novel effective X-path particle swarm optimization based deprived video data retrieval for smart city”, Clust Comput, pp. 1–10

  29. Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) "Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System." Exploring the Convergence of Big Data and the Internet of Things. IGI Global, 141-154. Web. 23 Apr. 2019. doi:https://doi.org/10.4018/978-1-5225-2947-7.ch011

  30. Yarmohammadi H, Rahmati M, Khadivi S (2013) ‘Content based video retrieval using information theory’, IEEE 8th Iranian Conference on Machine Vision and Image Processing, pp. 214–218

  31. Zemedkun Solomon CB, Sivaparthipan P, Punitha M, BalaAnand N (2018) Karthikeyan “certain investigation on power preservation in sensor networks” ," 2018 international conference on soft-computing and network security (ICSNS), Coimbatore, India, doi: 10.1109/ICSNS.2018.8573688

  32. Zhang HJ, Jianhua W, Di Z, Smoliar SW (1997) An integrated system for content-based video retrieval and browsing. Pattern Recognition, Pattern Recognition Society, Published by Elsevier Science Ltd 30(4):643–658

    Google Scholar 

  33. Zhu Q, Avidan S, Yeh M, Cheng K (2006) Fast human detection using a Cascade of histograms of oriented gradients. IEEE Conference on Computer Vision and Pattern Recognition 2006:1491–1498

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Anna University, University College of Engineering Dindigul, Dindigul, Tamilnadu, India

    R. Saravana Ram

  2. Department of Electrical and Electronics Engineering, Anna University, UniversityCollege of Engineering Ramanathapuram, Ramanathapuram, Tamilnadu, India

    S. Arun Prakash

  3. Department of Computer Science & Engineering, V.R.S. College of Engineering & Technology, Arasur, India

    M. Balaanand

  4. Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, India

    C. B. Sivaparthipan

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  1. R. Saravana Ram
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  2. S. Arun Prakash
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  3. M. Balaanand
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  4. C. B. Sivaparthipan
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Correspondence to R. Saravana Ram.

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Ram, R.S., Prakash, S.A., Balaanand, M. et al. Colour and orientation of pixel based video retrieval using IHBM similarity measure. Multimed Tools Appl 79, 10199–10214 (2020). https://doi.org/10.1007/s11042-019-07805-9

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  • DOI: https://doi.org/10.1007/s11042-019-07805-9

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