A video analysis on user feedback based recommendation using A-FP hybrid algorithm

  • R. G. SakthivelanEmail author
  • P. Rjendran
  • M. Thangavel


Video mining is an unsupervised finding of pattern in audio-visual content and also offers the optimized search based on event of interest associated to the target search over the search engine. Video mining is dawn related to other mining. Yet, the objective of existing search is to fetch a specific video from large database. Hence, our proposed goal is to retrieving of user’s requisite video based on an event is the major core problem in video mining. This paper propounds a new feedback relevance based video retrieval uses a hybrid of Apriori and Frequent Pattern (A-FP) algorithm creates a new methodology that gives the design of the learning. The A-FP algorithm desire to elicitation the most frequent item search which is pragmatic to the user. It also affords scalable solution for generalizing efficient and highly ambiguous user expected video search.


A-FP hybrid algorithm Event based recommender system Relevance feedback 



  1. 1.
    Anh TQ, Bao PT, Khanh TT, Ngo B, Thao D, Nhut NT, Tuan TA (2012) Video retrieval using histogram and sift combined with graph-based image segmentation. Inf Sci Lett 1(1):41–48CrossRefGoogle Scholar
  2. 2.
    Ansari A, Mohammed MH (2015) Content based video retrieval systems - methods, techniques, trends and challenges. Int J Comput Appl 112(7):0975–8887Google Scholar
  3. 3.
    Bhatt B, Patel PJ, Patel PJ (2014) A review paper on machine learning based recommendation system. IJEDR 2(4)Google Scholar
  4. 4.
    Chandra I, Sivakumar N, Gokulnath CB, Parthasarathy P (2018) IoT based fall detection and ambient assisted system for the elderly. Clust Comput:1–9Google Scholar
  5. 5.
    Dharmaraajan K, Dorairangaswamy MA (2016) Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data. In: IEEE International Conference on Advances in Computer Applications (ICACA)Google Scholar
  6. 6.
    He X, King O, Ma W-Y, Li M, Zhang H-J (2003) Learning a semantic space from user’s relevance feedback for image retrieval. IEEE transactions on circuits and systems for video technology 13(1)Google Scholar
  7. 7.
    Hoashi K, Matsumoto K, Inoue N (2003) Personalization of user profiles for content-based music retrieval based on relevance feedback. ACM, 1-58113-722-2/03/0011Google Scholar
  8. 8.
    Jiang L, Yu S-I, Meng D, Mitamura T, Hauptman AG (2015) Bridging the ultimate semantic gap: a semantic search engine for internet videos. ICMR’15.
  9. 9.
    Kanisha B, Lokesh S, Kumar PM, Parthasarathy P, Chandra Babu G (2018) Speech recognition with improved support vector machine using dual classifiers and cross fitness validation. Pers Ubiquit Comput:1–9Google Scholar
  10. 10.
    Kumar PM, Devi U, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162CrossRefGoogle Scholar
  11. 11.
    Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Futur Gener Comput Syst 86:527–534CrossRefGoogle Scholar
  12. 12.
    Lokesh S, Kumar PM, Devi MR, Parthasarathy P, Gokulnath C (2018) An automatic Tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Comput & Applic:1–11Google Scholar
  13. 13.
    Manasa G, Varsha K (2015) IAFP: integration of Apriori and FP-growth techniques to personalize data in web mining. International Journal of Scientific and Research Publications 5(7)Google Scholar
  14. 14.
    Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R (2018) A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des Autom Embed Syst:1–18Google Scholar
  15. 15.
    Padmavathy TV, Vimalkumar MN, Nagarajan S, Babu GC, Parthasarathy P (2018) Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform. Multimed Tools Appl:1–16Google Scholar
  16. 16.
    Parthasarathy P, Vivekanandan S (2018) A comprehensive review on thin film-based nano-biosensor for uric acid determination: arthritis diagnosis. World Review of Science, Technology and Sustainable Development 14(1):52–71CrossRefGoogle Scholar
  17. 17.
    Parthasarathy P, Vivekanandan S (2018) Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: a comprehensive review. Health information science and systems 6(1):19CrossRefGoogle Scholar
  18. 18.
    Parthasarathy P, Vivekanandan S (2018) A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Informatics in Medicine UnlockedGoogle Scholar
  19. 19.
    Priya S, Varatharajan R, Manogaran G, Sundarasekar R, Kumar PM (2018) Paillier homomorphic cryptosystem with poker shuffling transformation based water marking method for the secured transmission of digital medical images. Pers Ubiquit Comput:1–11Google Scholar
  20. 20.
    Rui Y, Huang TS, Mehrotra S (1997) Content-based image retrieval with relevance feedback in Mars. IEEE, 0-8186-8183-7Google Scholar
  21. 21.
    Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE transactions on circuits and systems for video technology 8(5)Google Scholar
  22. 22.
    Rui Y, Huang TS, Mehrotra S (2015) Relevance feedback techniques in interactive content-based image retrieval vol 3312. SPIE.
  23. 23.
    Singh P, Dahiya V (2015) A hybrid algorithm using Apriori growth and Fp-Split tree for web usage mining. 17(6). e-ISSN: 2278-0661, p-ISSN: 2278-8727
  24. 24.
    Su Z, Zhang H, Li S, Ma S (2003) Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans Image Process 12(8)Google Scholar
  25. 25.
    Sugiyama K, Hatano K, Yoshikawa M (2004) Adaptive web search based on user profile constructed without any effort from users. ACM 1-58113-844-X/04/0005Google Scholar
  26. 26.
    Sundarasekar R, Thanjaivadivel M, Manogaran G, Kumar PM, Varatharajan R, Chilamkurti N, Hsu CH (2018) Internet of things with maximal overlap discrete wavelet transform for remote health monitoring of abnormal ECG signals. J Med Syst 42(11):228CrossRefGoogle Scholar
  27. 27.
    Vijayakumar V, Priyan MK, Ushadevi G, Varatharajan R, Manogaran G, Tarare PV (2018) E-health cloud security using timing enabled proxy re-encryption. Mobile Networks and Applications:1–12Google Scholar
  28. 28.
    Wang Y, Guo Y, Chen Y (2016) Accurate and early prediction of user lifespan in an online video-on-demand system. 978-1-5090-1345-6/16 ©2016 IEEECrossRefGoogle Scholar
  29. 29.
    Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems ©. Springer-VerlagGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.AVS Engineering CollegeSalemIndia
  2. 2.Knowledge Institute of TechnologySalemIndia

Personalised recommendations