Skip to main content
SpringerLink
Log in

COLOR CHILD: a novel color image local descriptor for texture classification and segmentation

  • Short Paper
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Designing a robust image local descriptor for the purpose of image segmentation, analysis, recognition and classification has been an active area of research to date. In this paper, a robust and computationally efficient image local descriptor named “COLOR CHILD” has been proposed. COLOR CHILD addresses the weaknesses of Weber Local Descriptor (WLD) by considering Laplacian of Gaussian (LoG) for its differential excitation component and Tiansi fractional derivative-based filter for its orientation component. For any given image, these two components are then used to construct a concatenated histogram and with the addition of color moments up to third order the capabilities of the proposed descriptor COLOR CHILD has been extended to handle textures in color space. COLOR CHILD is shown to outperform all of the known state-of-the-art image local descriptors of parametric and non-parametric types on a variety of benchmark texture databases such as KTH-TIPS2-a, KTH-TIPS2-b, and CUReT under varying degrees of noise while performing texture classification task. Further, the response profile of the COLOR CHILD in terms of Wasserstein distance measures (obtained by sliding a query patch across the image to be segmented) is found to be better suited as initial image for active contour-based image and texture segmentation algorithms. The efficacy of the COLOR CHILD for segmentation task is amply demonstrated on synthetic color images under varying degrees of noise and on real-world texture images.

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
Fig. 17

References

  1. Richard OD, Peter EH, David GS (2001) Pattern Classification, Wiley

  2. Kaaniche M, Bremond F (2012) Recognizing gestures by Learning Local Motion Signatures of HOG Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence 34(12):2247–2258

  3. Chen J, Zhao G, Salo M, Rahtu E, Pietikainen M (2013) Automatic dynamic texture segmentation using local descriptors and optical flow. IEEE Trans Image Process 22(1):326–339

    Article  MathSciNet  Google Scholar 

  4. Shan Caifeng, Gong Shaogang, McOwan Peter W (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comp 27(6):803–816

    Article  Google Scholar 

  5. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Patt Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  6. Moreels P, Perona P (2007) Evaluation of feature detectors and descriptors based on 3D objects. Int J Comp Vision 73(3):263–284

    Article  Google Scholar 

  7. Felipe JC, Traina AJM, Traina C Jr (2003) Retrieval by content of medical images using texture for tissue identification, in proceedings of IEEE Symposium on Computer-Based Medical Systems, New York, pp 175–180

  8. Yue J, Zhenbo LL, Liu ZF (2011) Content-based image retrieval using color and texture fused features. Math Comp Model 54(3–4):1121–1127

    Article  Google Scholar 

  9. Roland T, Chinand Charles RD (1986) Model-based recognition in robot vision. ACM Comp Surveys 18(1):67–108

    Article  Google Scholar 

  10. Navneet D, Bill T (2005) Histograms of Oriented Gradients for Human Detection, in proceedings of IEEE International conference on Computer Vision and Pattern Recognition CVPR

  11. Lowe D (2004) Distinctive image features from scale invariant key points. Int J Comp Vision 60(2):91–110

    Article  Google Scholar 

  12. Yan K, Rahul S (2004) PCA-SIFT: A More Distinctive Representation for Local Image Descriptors, in proceedings of IEEE International Conference on Computer Vision and Pattern Recogntition

  13. Dongliang S, Jian W, Cui Z, Sheng VS, Gong S (2013) CGCI-SIFT: a more efficient and compact representation of local descriptor. Measur Sci Rev 13(3):132–141

    Google Scholar 

  14. Lazebnik S, Schmid C, Ponce J (2005) A Maximum Entropy Framework for Part-Based Texture and Object Recognition, in proceedings of IEEE International Conference on Computer Vision

  15. Dorko G, Schmid C (2006) Maximally Stable Local Description for Scale Selection, in proceedings of European Conference on Computer Vision

  16. Manjunath B, Ma W (1996) Texture features for browsing and retrieval of image data. IEEE Trans Patt Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  17. Ojala T, Pietikainen M, Harwood DA (1996) Comparative study of texture measures with classification based on feature distributions. Patt Recogn 29(1):51–59

    Article  Google Scholar 

  18. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray scale and rotation invariant texture analysis with local binary patterns. IEEE Trans Patt Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  19. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) WLD: a robust local imagedescriptor. IEEE Trans Patt Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  20. Liu F, Tang Z, Tang J (2013) WLBP: Weber local binary pattern for local image description. Neuro Comp 120:325–335

    Google Scholar 

  21. Zhang J, Liang J, Zhao H (2013) Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans Image Process 22(1):31–42

    Article  MathSciNet  Google Scholar 

  22. Yimo G, Guoying Z, Matti P (2011) Texture Classification using a Linear Configuration Model based Descriptor, in proceedings of British Machine Vision Conference, BMVC

  23. Zhang J, Zhang H, Liang J (2013) Continuous rotation invariant local descriptors for texton dictionary-based texture classification. Comp Vision Image Underst 117(1):56–75

    Article  Google Scholar 

  24. Maani R, Kalra S (2013) Yee-Hong Yang. Rotation Invariant Local Frequency Descriptors for Texture Classification, IEEE Transactions on Image Processing 22(6):2409–2419

  25. Liquang N, Shuicheng Y, Meng W, Richang H, Tat-Seng C (2012) Harvesting visual concepts for image search with complex queries, in proceedings of ACM international conference on Multimedia, pp 59–68

  26. Lofti T, Mounir S, Farhat F (2010) A New Descriptor for Texture Image Segmentation based on Fuzzy Type-2 Clustering Approach, in proceedings of International Conference on Image Processing Theory Tools and Applications (IPTA), pp 258–263

  27. Jie C, Guoying Z, Matti P (2009) An Improved Local Descriptor and Threshold Learning for Unsupervised Dynamic Texture Segmentation, in proceedings of IEEE International Conference on Computer Vision Workshops, pp 460–467

  28. Sasidharan R, Menaka D (2013) Dynamic texture segmentation of video using texture descriptors and optical flow of pixels for automating monitoring in different environments, in proceedings of International Conference on Communications and Signal Processing, pp 841–846

  29. Derraz F, Thiran JP, Taleb-Ahmed A, Peyrodie L, Forzy G (2012) Fast Globally Supervised Segmentation by Active Contours With Shape And Texture Descriptors, in proceedings of IEEE International Conference in Image Processing, pp 2545–2548

  30. Kokkinos I, Evangelopoulos G, Maragos P (2009) Analysis texture, features segmentation using modulation, models generative, evolution weighted curve. IEEE Trans Patt Anal Mach Intell 31(1):145–157

    Article  Google Scholar 

  31. Idrissi SY (2013) Samir Belfkih. Texture Image Segmentation using a New Descriptor and Mathematical Morphology. Int Arab J Infor Technol 10(2):204–208

  32. Anil K (1989) Jain. Prentice Hall, Fundamentals of DIgital Image Processing

  33. Shen J (2003) On the foundations of vision modeling: Weber’s law and Weberized TV Restoration. Physica D: Nonlinear Phenomena 175(3–4):241–251

    Article  MathSciNet  MATH  Google Scholar 

  34. Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on Weber’s law with application to face recognition. IEEE Signal Process Lett 18(8):462–465

    Article  Google Scholar 

  35. Bruni V, Vitulano D (2004) A generalized model for scratch removal. IEEE Trans Image Process 13(1):44–50

    Article  Google Scholar 

  36. Sun S, Zhao L (2013) Shicai Yang. Mathematical Problems in Engineering, Gabor wavelet Local Descriptor for Bovine Iris Recognition, pp 1–7

  37. Liu L, Paul F, Gangyao K (2011) Generalized Local Binary Patterns for Texture Classification, in proceedings of the British Machine Vision Conference BMVC, pp 1–11

  38. Mathieu B, Melchior P, Oustaloup A, Ceyral Ch (2003) Fractional differentiation for edge detection. Signal Process 83:2421–2432

    Article  MATH  Google Scholar 

  39. You J, Hungnahally S, Sattar A (1997) Fractional discrimination for texture image segmentation, in proceedings of International Conference on Image Processing ICIP, Santa Barbara, 220–223

  40. Anamandra Sai Hareesh , V. Chandrasekaran (2014) Exemplar-based color image inpainting: a fractional gradient function approach, Pattern Analysis and Applications 17(2):389–399

  41. Djurovic I, Stankovic S, Pitas I (2001) Digital watermarking in the fractional Fourier transformation domain. J Network Comp Appl 24(2):167–173

    Article  MATH  Google Scholar 

  42. Ghasemi S, Tabesh A, Askari-Marnani J (2014) Application of fractional calculus theory to robust controller design for wind turbine generators. IEEE Trans Energy Conv 29(3):780–787

    Article  Google Scholar 

  43. Machado JA (2014) Tenreiro, Baleanu, Dumitru, Luo. Albert C. J, Discontinuity and Complexity in Nonlinear Physical Systems, Springer

  44. Loverro A, Calculus F (2004) History. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Definitions and Applications for the Engineer

  45. Yang Z, Lang F, Xiaohong Y, Zhang Y (2011) The construction of fractional differential gradient operator. J Comp Inform Syst 7(12):4328–4342

    Google Scholar 

  46. Georgiou T, Michailovich O, Rathi Y, Malcolm J, Tannenbaum A (2007) Distribution metrics and image segmentation. Linear Algebra and its Applications 405:663–672

  47. Ni K, Bresson X, Chan T, Esedoglu S (2009) Local Histogram Based Segmentation Using the Wassertain Distance. Int J Comp Vision 84:97–111

    Article  Google Scholar 

  48. Francesca P. Carli, Lipeng Ning, Tryphon T.Georgiou (2013) Approximation in the Wasserstein Distance with Application to Clustering, http://arxiv.org/pdf/1307.5459v1

  49. Schmitzer B, Schnorr C (2013) Modelling Convex Shape Priors and Matching Based on the Gromov-Wasserstein Distance. J Math Imaging Vision 46(1):143–159

    Article  MathSciNet  MATH  Google Scholar 

  50. Manjunath BS, Jens-Rainer O, Vinod VV, Akio Y (2001) Color and Texture Descriptors, IEEE Transactions on Circuits and Systems For Video Technology 11(6):703–715

  51. Idrissi K, Lavoue G, Ricard J, Baskurt A (2004) Object of interest based visual navigation, retrieval and semantic content identification system, Computer Vision On Image Understanding, 94(1–3)

  52. Markus S, Markus O (1995) Similarity of Color Images, in proceedings of SPIE, San Jose

  53. Barbara C, Eric H, Mallikarjuna P (2005) Class-Specific Material Categorisation, in proceedings of International Conference on Computer Vision

  54. KTH-TIPS2-b texture database downloaded from http://www.nada.kth.se/cvap/databases/kth-tips/index.html

  55. Dana KJ, Van-Ginnekan B, Nayar SK, Koenderink JJ (1999) Reflectance and Texture of Real World Surfaces. ACM Transa Graph 18(1):1–34

    Article  Google Scholar 

  56. Kylberg G, Sintorn I-M (2013) Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Video Process 17:1–20

    Google Scholar 

  57. Liao S (2009) Max WK Law. Albert CS Chung, Dominant local binary patterns for texture classification, IEEE Transactions on Image Processing 18(5):1107–1118

  58. Liu L, Fieguth PW (2012) Texture Classification from Random Features. IEEE Trans Patt Anal Mach Intell 34(3):574–586

    Article  Google Scholar 

  59. Srikanth K,Chandrasekaran V (2012) Fractional Derivative Filter For Image Contrast Enhancement With Order Prediction, in proceedings of IET International Conference on Image Processing, London

  60. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors like to dedicate this work to the Founder Chancellor of their university Bhagawan Sri Sathya Sai Baba. The author is supported by the inspire fellowship by the Department of Science and Technology, Govt. of India.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India

    Sai Hareesh Anamandra & V. Chandrasekaran

Authors
  1. Sai Hareesh Anamandra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Chandrasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sai Hareesh Anamandra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anamandra, S.H., Chandrasekaran, V. COLOR CHILD: a novel color image local descriptor for texture classification and segmentation. Pattern Anal Applic 19, 821–837 (2016). https://doi.org/10.1007/s10044-015-0528-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-015-0528-5

Keywords

Search

Navigation