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Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval

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Abstract

In the recent past, many local texture descriptors have been proposed for the image retrieval task. In order to improve the image retrieval accuracy, quite a few of these descriptors have been implemented on Gabor filter response. However, the response of Log-Gabor filters has been proved to be better than Gabor filters with respect to their discrimination ability. In this paper, we present a framework for image retrieval that applies various local texture descriptors on Log-Gabor filters response. To evaluate the retrieval performance of the proposed framework, experiments have been conducted on standard Wang, VisTex and OT-Scene databases. Consistent improvement in the image retrieval accuracy demonstrates the effectiveness of this framework. Further, the experimental results show that the use of proposed framework with low-dimension texture descriptors such as Orthogonal Combination of Local Binary Pattern makes them a better choice over Local Binary Pattern and its high-dimensional variants when higher retrieval accuracy, small feature vector size and ease of computation is desired.

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References

  1. Kato T (1992) Database architecture for content-based image retrieval. In SPIE/IS&T 1992 symposium on electronic imaging: science and technology, pp 112–123

  2. Rui Y, Thomas SH, Shih-Fu C (1999) Image retrieval: current techniques, promising directions and open issues. J Vis Commun Image Represent 10:39–62

    Article  Google Scholar 

  3. Long F, Zhang H, Feng DD (2003) Fundamentals of content-based image retrieval. In: Multimedia information retrieval and management. Signals and communication technology, Springer, Berlin, pp 1–26

  4. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl (TOMM) 2(1):1–19

    Article  Google Scholar 

  5. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv (CSUR) 40(2) (article 5)

  6. Ahmad A, Amira A, Ramzan N (2015) Semantic content-based image retrieval: a comprehensive study. J Vis Commun Image Represent 32:20–54

    Article  Google Scholar 

  7. Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recognit 36(3):665–679

    Article  MathSciNet  Google Scholar 

  8. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  9. Fogel I, Sagi D (1989) Gabor filters as texture discriminator. Biol Cybern 61(2):103–113

    Article  Google Scholar 

  10. Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation using Gabor filters. Pattern Recognit 24(12):1167–1186

    Article  Google Scholar 

  11. Nava R, Escalante-Ramírez B, Cristóbal G (2012) Texture image retrieval based on log-Gabor features. In: Progress in pattern recognition, image analysis, computer vision, and applications. Springer, Berlin, pp 414–421

  12. Gabor D (1946) Theory of communication. Part 1: The analysis of information. J Inst Electr Eng 93(26):429–441

    Google Scholar 

  13. Field DJ (1987) Relation between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4(12):2379–2394

  14. Kovesi P (2015) What are log-Gabor filters and why are they good? http://www.peterkovesi.com/matlabfns/PhaseCongruency/Docs/convexpl.html. Accessed June 2015

  15. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognit 29(1):51–59

    Article  Google Scholar 

  16. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: Computer vision—ECCV 2004. Springer, Berlin, pp 469–481

  17. Mäenpää T, Turtinen M, Pietikäinen M (2003) Real-time surface inspection by texture. Real-Time Imaging 9(5):289–296

    Article  MATH  Google Scholar 

  18. Mäenpää T (2003) The local binary pattern approach to texture analysis: extenxions and applications. Doctoral dissertation, University of Oulu

  19. Satpathy A, Jiang X, Eng HL (2014) LBP-based edge-texture features for object recognition. IEEE Trans Image Process 23(5):1953–1964

    Article  MathSciNet  Google Scholar 

  20. Zhu C, Bichot CE, Chen L (2013) Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recognit 46(7):1949–1963

    Article  Google Scholar 

  21. Vipparthi SK, Murala S, Nagar SK, Gonde AB (2015) Local Gabor maximum edge position octal patterns for image retrieval. Neurocomputing 167:336–345

    Article  Google Scholar 

  22. Patil S, Talbar S (2012) Content based image retrieval using various distance metrics. In: Data engineering and management. Springer, Berlin, pp 154–161

  23. Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

  24. MIT Vision and Modeling Group (2015) Vision texture. http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html. Accessed November 2015

  25. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  26. Zhang D, Lu G (2002) Shape-based image retrieval using generic Fourier descriptor. Signal Process Image Commun 17(10):825–848

    Article  Google Scholar 

  27. Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Article  Google Scholar 

  28. Wyszecki G, Styles WS (1982) Color science: concepts and methods. Quantitative data and formulae. Wiley, New York

    Google Scholar 

Download references

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Correspondence to Vishal Verma.

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Walia, E., Verma, V. Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval. Int J Multimed Info Retr 5, 173–184 (2016). https://doi.org/10.1007/s13735-016-0099-2

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  • DOI: https://doi.org/10.1007/s13735-016-0099-2

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