Nonlinear local contourlet energy pattern for image retrieval applications

  • Abstract
  • Keywords
  • References
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  • Abstract

    Local patterns are effective in different machine vision problems such as pedestrian identification, lane categorization, face recognition, retrieving required image etc., Various local patterns are introduced by the researchers in order improve the efficiency, however these local pattern operates on a fixed pixels that are predetermined and are same for all the images. The features thus extracted from these predetermined pixels are limited. In this paper a novel technique called nonlinear local contourlet energy pattern (NLCEP) is introduced which extracts the local pattern from the pixels that are selected dynamically in run time which will vary with the images. Also to improve the feature robustness the features are extracted in Contourlet domain instead of the spatial domain. With this approach the dominant image features like lines/curves are better represented by the NLCEP and its features are effectively used in image retrieval system. The performance of this method is validated by doing different experiments with the standard available databases (viz Corel 1K, Corel 10K and Brodatz). The test results with different experiments shows that the proposed approach provides better performance for image retrieval applications.



  • Keywords

    Local Pattern; Nonlinear Local Contourlet Energy Pattern; Local Tetra Pattern; Content Based Image Retrieval.

  • References

      [1] Li Liua, et al (2012), extended local binary patterns for texture classification, Vol 30, Image and Vision Computing, pp 86–99.

      [2] MH Shakoor and Reza Boostani (2017), Extended Mapping Local Binary Pattern Operator for Texture Classification, Vol 31, International Journal of Pattern Recognition and Artificial Intelligence.

      [3] Yang Hongbo, Hou Xia (2014), Histogram modification using grey-level co-occurrence matrix for image contrast enhancement, Vol: 8, IET Image Processing, pp. 782 – 793.

      [4] T.N. Tan, K.D. Baker (2000), efficient image gradient based vehicle localization, Vol: 9, IEEE Transactions on Image Processing, pp. 1343 – 1356.

      [5] I.S. Uzun, A. Amira, A. Bouridane (2005), FPGA implementations of fast Fourier transforms for real-time signal and image processing, Vol: 152, IEE Proceedings - Vision, Image and Signal Processing, pp. 283 – 296.

      [6] Y. Asnath Victy Phamila, R. Amutha (2014), Discrete Cosine Transform based fusion of multi-focus images for visual sensor networks, Vol: 95, Signal Processing, pp. 161–170.

      [7] M W Jian et al (2011), Image retrieval using wavelet-based salient regions, Vol. 59, The Imaging Science Journal, pp. 219-231.

      [8] Qiangui Huang, Boya Hao, Sheng Chang (2016), Adaptive digital ridgelet transform and its application in image denoising, Vol. 52, Elsevier Digital Signal Processing, pp. 45–54.

      [9] Melkamu Hunegnaw Asmare, Vijanth S. Asirvadam, Ahmad Fadzil M. Hani (2015), Image enhancement based on contourlet transform, Vol 9, Signal, Image and Video Processing, pp 1679–1690.

      [10] Wei Tian et al (2015), An adaptive blind digital watermarking scheme in shearlet domain, Vol. 8, Int. J. of Wireless and Mobile Computing, pp. 353 – 358.

      [11] James M. Murphy, Jacqueline Le Moigne, David J. Harding (2016), Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features, Vol. 54, IEEE Transactions on Geoscience and Remote Sensing, pp. 1685 – 1704.

      [12] Manesh Kokare et al (2005), Cosine-Modulated Wavelet Packet based Texture Features for Content based Image Retrieval, Vol. 51, IETE Journal of Research, pp. 477-483.

      [13] Yongsheng Dong et al. (2015), Texture Classification and Retrieval Using Shearlets and Linear Regression, Vol. 45, IEEE Transactions on Cybernetics, pp. 358 – 369.

      [14] Shiv Ram Dubey eta l (2015), Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases, Vol. 24, IEEE Transactions on Image Processing, pp. 5892 – 5903.

      [15] Jiangping He, Hongwei Ji, Xin Yang (2013), Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms, Vol. 20, IEEE Signal Processing Letters, pp. 905 – 908.

      [16] Amani Alahmadi et al (2017), Passive detection of image forgery using DCT and local binary pattern, Vol 11, Signal, Image and Video Processing, pp 81–88.

      [17] Santosh Kumar Vipparthi et al (2015), Directional local ternary patterns for multimedia image indexing and retrieval, Vol. 8, Int. J. of Signal and Imaging Systems Engineering, pp. 137 – 145.

      [18] Srinivasa Perumal Ramalingam et al (2016), Two-level dimensionality reduced local directional pattern for face recognition, Vol. 8, Int. J. of Biometrics, pp. 52 – 64.

      [19] Shankar Bhausaheb Nikam (2008), Local binary pattern and wavelet-based spoof fingerprint detection, Vol. 1, Int. J. of Biometrics, pp. 141 – 159.

      [20] Joao B.Florindo, Odemir M.Bruno (2016), Local fractal dimension and binary patterns in texture recognition, Vol 78, Pattern Recognition Letters, pp. 22-27.

      [21] Muhammad Hussain et al (2013), Gender recognition from face images with dyadic wavelet transform and local binary pattern, Vol. 22, International Journal on Artificial Intelligence Tools.

      [22] S. He, J. J. Soraghan, B. F. O’Reilly, and D. Xing (2009), Quantitative analysis of facial paralysis using local binary patterns in biomedical videos, Vol. 56, IEEE Trans. Biomed. Eng., pp. 1864-1870.

      [23] Loris Nannia, Alessandra Luminia, Sheryl Brahnam (2010), Local binary patterns variants as texture descriptors for medical image analysis Vol. 49, Artificial Intelligence in Medicine, pp. 117–125.

      [24] A. Suruliandi, G. Murugeswari, and P. Arockia Jansi Rani (2015), Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images, Vol. 15, International Journal of Image and Graphics

      [25] Wankou Yang et al. (2016), Face recognition using adaptive local ternary patterns method, Vol 213, Neurocomputing, pp. 183-190.

      [26] Guo, L. Zhang, and D. Zhang (2010), A completed modelling of local binary pattern operator for texture classification, Vol. 19, IEEE Trans. Image Process, pp. 1657–1663.

      [27] S. Liao, M.W. K. Law, and A. C. S. Chung (2009), Dominant local binary patterns for texture classification, Vol. 18, IEEE Trans. Image Process, pp. 1107–1118.

      [28] G. Deep et al (2017), Local quantized extrema quinary pattern: a new descriptor for biomedical image indexing and retrieval, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp 1-17.

      [29] Timo Ahonen et al, "Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features", SCIA '09 Proceedings, July 2009.

      [30] Maryam Nabil Al-Berry et al Salem (2016), Fusing directional wavelet local binary pattern and moments for human action recognition, Vol. 10, IET Computer Vision, pp. 153 – 162.

      [31] Lijian Zhou et al (2014), Face recognition based on curvelets and local binary pattern features via using local property preservation, Vol. 95, Journal of Systems and Software, pp. 209-216.

      [32] Hemprasad Y.Patil et al (2016), Expression invariant face recognition using local binary patterns and contourlet transform, Vol. 127, International Journal for Light and Electron Optics, pp. 2670-2678.

      [33] Jiangping He et al (2013), Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms, Vol. 20, IEEE Signal Processing Letters, pp. 905 – 908.

      [34] Murala, Maheshwari, Balasubramanian, "Local tetra patterns: a new feature descriptor for content-based image retrieval", IEEE Trans Image Process. 2012 May; 21(5):2874-86.

      [35] Ojala, T., Pietikainen, M., Maenpaa, T. (2002), Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell. pp. 971–987.

      [36] T. Ahonen, A. Hadid, and M. Pietikainen (2006), Face description with local binary patterns: Applications to face recognition, Vol. 28, IEEE Trans. Pattern Anal. Mach. Intell., pp. 2037-2041.

      [37] Corel 1K database from

      [38] Corel 10K database from /Home/content-based-image-retrieval.

      [39] Brodatz database original_brodatz.html.

      [40] Shiv Ram Dubey et al (2015), Boosting local binary pattern with bag-of-filters for content based image retrieval, IEEE UP Section Conference on Electrical Computer and Electronics (UPCON).

      [41] Subrahmanyam Murala, Maheshwari, Balasubramanian (2012), Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval, Vol. 36, Journal of Medical Systems, pp. 2865-2879.




Article ID: 16832
DOI: 10.14419/ijet.v7i4.16832

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