Skip to main content
SpringerLink
Log in

Performance evaluation of the combination of Compacted Dither Pattern Codes with Bhattacharyya classifier in video visual concept depiction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

High dimensionality and multi-feature combinations can have negative effect on visual concept classification. In our research, we formulated a new compacted form which is Compacted Dither Pattern Code (CDPC) as a chromatic syntactic feature for visual feature extraction. The effectiveness of CDPC with Bhattacharyya classifier for irregular shapes based visual concepts depiction is reported in this paper. The proposed technique can reduce feature space and computational complexity while maintaining visual data mining and retrieval accuracy in high standard. Our system was empowered with Bhattacharyya classifier which has improved efficiency by considering one numeric value which is the Bhattacharyya coefficient. Experiments were conducted on various combinations and compared with different visual descriptors and classifiers. The first experiment illustrates the comparison of the CDPC based results with well known feature space reduction classes. The second and third experiments demonstrate the effectiveness of our approach with multiple perspectives of performance measures including various concepts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Badjio FE, Poulet F (2005) Dimension reduction for visual data mining. In International symposium on applied stochastic models and data analysis (ASMDA-2005)

  2. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-Up Robust Features (SURF). Comput Vis Image Underst 110(3):346–359, Elsevier publication

    Article  Google Scholar 

  3. Bhattacharyya A (1946) On a measure of divergence between two multinomial populations. Sankhyā: The Indian Journal of Statistics (1933–1960), Vol. 7, No. 4 (Jul., 1946), Published by Indian Statistical Institute, pp 401–406

  4. Chattopadhyay A, Chattopadhyay AK, Rao CB (2004) Bhattacharyya’s distance measure as a precursor of genetic distance measures. J Biosci 29(2):135–138, June 2004, Indian Academy of Sciences

    Article  Google Scholar 

  5. Currier B (1995) Digital Video Codec Choices. Tips & Articles, Synthetic Aperture, Available: http://www.synthetic-ap.com/qt/, 1995

  6. Datta R, Li J, Wang JZ (2005) Content-based image retrieval—approaches and trends of the new age. Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, Singapore, November 11–12, 2005, pp 253–262

  7. Gao Y, Fan J (2005) Semantic image classification with hierarchical feature subset selection. Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pp 135–142

  8. Gao S, Zhu X, Sun Q (2007) Exploiting concept association to boost multimedia semantic concept detection. ICASSP 2007, Volume: 1, pp 981–984

  9. Halkos D, Doulamis N, Doulamis A (2009) A secure framework exploiting content guided and automated algorithms for real time video searching. Multimedia Tools Appl 42:343–375

    Article  Google Scholar 

  10. Heller AK, Ghahramani Z (2006) A simple Bayesian framework for content-based image retrieval. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp 2110–2117

  11. Hsu C, Chang C, Lin C (2008) A practical guide to support vector classification. Department of Computer Science National Taiwan University, Taipei 106, Taiwan, available via http://www.csie.ntu.edu.tw/∼cjlin

  12. Israel M, Broek ELVD, Putten PVD (2004) Automating the construction of scene classifiers for content based video retrieval. Proceedings of the Fifth International Workshop on Multimedia Data Mining (MDM/KDD’04), August 22, 2004, pp 38–47

  13. Jiang YG, Zhao WL, Ngo CW (2006) Exploring semantic concept using local invariant features. Asia-Pacific Workshop on Visual Information Processing, VIP06, November 2006

  14. Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. Proceedings of International Conference on Computer Vision and Pattern Recognition, Vol. 2, IEEE Computer Society, pp 506–513

  15. Konstantinou N, Solidakis E, Zafeiropoulos A, Stathopoulos P, Mitrou N (2009) A context-aware middleware for real-time semantic enrichment of distributed multimedia metadata. Multimedia Tools Appl 46:425–461

    Article  Google Scholar 

  16. Lavee G, Khan L, Thuraisingham B (2007) A framework for a video analysis tool for suspicious event detection. Multimedia Tools Appl 35:109–123

    Article  Google Scholar 

  17. Lew MS (2000) Next generation web searches for visual content. IEEE Computer, November 2000, pp 46–53

  18. Lin L, Ravitz G, Shyu M, Chen S (2008) Effective feature space reduction with imbalanced data for semantic concept detection. IEEE International conference on sensor networks, Ubiquitous and trustworthy computing 2008, IEEE Computer Society, pp 262–269

  19. Lindstaedt S, Mörzinger R, Sorschag R, Pamme V, Thallinger G (2009) Automatic image annotation using visual content and folksonomies. Multimedia Tools Appl 42:97–113

    Article  Google Scholar 

  20. Michael SL, Nicu S, Chabane D, Ramesh J (2006) Content-based multimedia information retrieval: State of the Art and Challenges. In ACM Transactions on Multimedia Computing, Communications and Applications, Vol 2, Issue 1, pp 1–19

  21. Mikolajczyk K, Leibe B, Schiele B (2005) Local features for object class recognition. Proceedings of the 10th IEEE International Conference on Computer Vision, Vol. 2, pp 1792–1799

  22. Mittal A (2006) An overview of multimedia content-based retrieval strategies. Informatica 30:347–356

    MATH  MathSciNet  Google Scholar 

  23. Mr´owka E, Dorado A, Pedrycz W, Izquierdo E (2004) Dimensionality reduction for content-based image classification. Eighth International Conference on Information Visualisation, pp 435–438

  24. Nguyen GP, Worring M (2007) Optimization of interactive visual-similarity-based search. ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 4, No. 1, Article 7, 23 pages

  25. NIST (2008) Guidelines for the TRECVID 2008 Evaluation. Available via http://www-nlpir.nist.gov/projects/tv2008/tv2008.html#1. Accessed on 5 Jan 2009

  26. Sebe N (2003) Multimedia information retrieval: promises and challenges. Proceeding of 5th ACM SIGMM International Workshop on Multimedia Information Retrieval ACM New York

  27. Sebe N, Lew MS (2001) Salient points for content based retrieval. Proceedings of the British Machine Vision Conference 2001, BMVC 2001, Manchester, UK, British Machine Vision Association 2001, pp 401–410

  28. Smeulders AWM et al (2006) Adaptive high-performance distributed multimedia computing. Proposal for NWO GLANCE, 2006

  29. Snoek C et al (2005) The MediaMill TRECVID 2005 Semantic Video Search Engine. In Proceedings of the 3rd TRECVID Workshop, Nov. 2005

  30. Snoek C, Worring M, Hauptmann A (2006) Learning rich semantics from news video archives by style analysis. ACM Trans Multimedia Computing, Comm Applications 2(2):91–108

    Article  Google Scholar 

  31. Spyrou E, Avrithis Y (2007) High-level concept detection in video using a region thesaurus. Emerging Artificial Intelligence Applications in Computer Engineering, Series in Frontiers in Artificial Intelligence and Applications, IOS Press, Amsterdam, Netherlands, in print

  32. Srinivasan U, Pfeiffer S, Nepal S, Lee M, Gu L, Barrass S (2005) A survey of MPEG-1 audio, video and semantic analysis techniques. Multimedia Tools Appl 27:105–141

    Article  Google Scholar 

  33. Xiong Z, Zhou XS, Tian Q, Rui Y, Huang TS (2006) Semantic retrieval of video: review of research on video retrieval in meetings, movies and broadcast news, and sports. IEEE Signal Process Mag 23(2):18–27

    Article  Google Scholar 

  34. Zheng W, Li J, Si Z, Lin F, Zhang B (2006) Using high-level semantic features in video retrieval. CIVR 2006, LNCS 4071, pp 370–379

  35. Zhu S, Liu Y (2009) Scene segmentation and semantic representation using a novel scheme. Multimedia Tools Appl 42:183–205

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Lochandaka Ranathunga, Roziati Zainuddin & Nor Aniza Abdullah

Authors
  1. Lochandaka Ranathunga
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roziati Zainuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nor Aniza Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lochandaka Ranathunga.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ranathunga, L., Zainuddin, R. & Abdullah, N.A. Performance evaluation of the combination of Compacted Dither Pattern Codes with Bhattacharyya classifier in video visual concept depiction. Multimed Tools Appl 54, 263–289 (2011). https://doi.org/10.1007/s11042-010-0522-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0522-2

Keywords

Search

Navigation