Local binary patterns (LBPs) are a well-known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi-level and multi-resolution (ML + MR) approach and the ML, MR and multi-band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi-2 similarity measurement with Czekannowski improves the accuracy of texture classification.

References

1. 1)
  - 37. Xiao, F., Liang, Y., Qu, X.: ‘Learning local binary patterns with enhanced boosting for face recognition’. Seventh Int. Conf. on Computational Intelligence and Security (CIS), 2011.
2. 2)
  - 49. Cha, S.-H.: ‘Taxonomy of nominal type histogram distance measures’. American Conf. on Applied Mathematics (MATH ‘08), 2008.
3. 3)
  - 45. Brodatz, P.: ‘Textures: a photographic album for artists and designers’ (Dover Publications, New York, NY, 1966).
4. 4)
  - 21. Shengping, Z., Hongxun, Y., Shaohui, L.: ‘Dynamic background modeling and subtraction using spatio-temporal local binary patterns’. 15th IEEE Int. Conf. on Image Processing, 2008. ICIP 2008, 2008.
5. 5)
  - 41. Ren, J., Jiang, X., Yuan, J.: ‘Noise-resistant local binary pattern with an embedded error-correction mechanism’, IEEE Trans. Image Process., 2013, 22, (10), pp. 4049–4060 (doi: 10.1109/TIP.2013.2268976).
6. 6)
  - 46. Available at http://vismod.media.mit.edu/pub/, accessed 1995.
7. 7)
  - 57. Malpica, N., Ortuño, J.E., Santos, A.: ‘A multichannel watershed-based algorithm for supervised texture segmentation’, Pattern Recognit. Lett., 2003, 24, (9–10), pp. 1545–1554 (doi: 10.1016/S0167-8655(02)00393-8).
8. 8)
  - 31. Fei-Fei, L., Perona, P.: ‘A Bayesian hierarchical model for learning natural scene categories’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005. CVPR 2005, 2005.
9. 9)
  - 9. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on feature distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59 (doi: 10.1016/0031-3203(95)00067-4).
10. 10)
  - 13. Yang, F., Lu, H., Zhang, W., Yang, G.: ‘Visual tracking via bag of features’, IET Image Process., 2012, 6, (2), pp. 115–128 (doi: 10.1049/iet-ipr.2010.0127).
11. 11)
  - 15. Juca, V., Mello, C.: ‘Texture-based segmentation using image fidelity indexes’, IEEE (Rev. IEEE Am. Latina) Latin Am. Trans., 2011, 9, (3), pp. 423–428 (doi: 10.1109/TLA.2011.5893792).
12. 12)
  - 10. Li, L., Fieguth, P.: ‘Texture classification from random features’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (3), pp. 574–586 (doi: 10.1109/TPAMI.2011.145).
13. 13)
  - 2. Caputo, B., Hayman, E., Fritz, M., Eklundh, J.O.: ‘Classifying materials in the real world’, Image Vis. Comput., 2010, 28, (1), pp. 150–163 (doi: 10.1016/j.imavis.2009.05.005).
14. 14)
  - 26. Bosch, A., Zisserman, A., Muñoz, X.: ‘Representing shape with a spatial pyramid kernel’ Proc. 6th ACM Int. Conf. on Image and Video Retrieval, Amsterdam, The Netherlands, 2007, pp. 401–408.
15. 15)
  - 24. Lazebnik, S., Schmid, C., Ponce, J.: ‘Beyond bags of features: spatial pyramid matching for recognizing natural scene categories’. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2006.
16. 16)
  - 39. Guo, Z., Zhang, L., Zhang, D.: ‘A completed modeling of local binary pattern operator for texture classification’, IEEE Trans. Image Process., 2010, 9, (16), pp. 1657–1663.
17. 17)
  - 56. Topi, M., Matti, P., Timo, O.: ‘Texture classification by multi-predicate local binary pattern operators’. 15th Int. Conf. on Pattern Recognition, 2000. Proc., 2000.
18. 18)
  - 22. Ojala, T., Pietikainen, M., Maenpaa, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987 (doi: 10.1109/TPAMI.2002.1017623).
19. 19)
  - 3. Ferrer, M.A., Vargas, J.F., Morales, A., Ordonez, A.: ‘Robustness of offline signature verification based on gray level features’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (3), pp. 966–977 (doi: 10.1109/TIFS.2012.2190281).
20. 20)
  - 22. Heikkilä, M., Pietikäinen, M., Heikkilä, J.: ‘A texture-based method for detecting moving objects’. British Machine Vision Conf. (BMVC), 2004.
21. 21)
  - 25. Qian, X., Liu, G., Guo, D., Li, Z., Wang, Z., Wang, H.: ‘HWVP: hierarchical wavelet packet descriptors and their applications in scene categorization and semantic concept retrieval’, Multimed. Tools Appl., 2014, 69, (3), pp. 897–920 (doi: 10.1007/s11042-012-1151-8).
22. 22)
  - 6. Rowshan, B.R.: ‘Texture classification in underwater images’ (MSc Erasmus Mundus in Vision and RoBOTics (ViBOT), Universitat de Girona, Spain, 2011).
23. 23)
  - 1. Paiement, A., Mirmehdi, M., Xianghua, X., Hamilton, M.C.K.: ‘Integrated segmentation and interpolation of sparse data’, IEEE Trans. Image Process., 2014, 23, (1), pp. 110–125 (doi: 10.1109/TIP.2013.2286903).
24. 24)
  - 51. Hua, Y., Xiao-Ping, Z.: ‘Statistical modeling in the wavelet domain for compact feature extraction and similarity measure of images’, IEEE Trans. Circuits Syst. Video Technol., 2010, 20, (3), pp. 439–445 (doi: 10.1109/TCSVT.2009.2031396).
25. 25)
  - 17. Unay, D., Ekin, A., Jasinschi, R.S.: ‘Local structure-based region-of-interest retrieval in brain Mr images’, IEEE Trans. Inf. Technol. Biomed., 2010, 14, (4), pp. 897–903 (doi: 10.1109/TITB.2009.2038152).
26. 26)
  - 47. Available at http://www.cssip.elec.uq.edu.au/~guy/meastex/meastex.html, accessed 1998.
27. 27)
  - 5. Mathew, B., Davis, A., Evans, R.: ‘A characterization of visual feature recognition’. IEEE Sixth Annual Workshop on Workload Characterization, 2003.
28. 28)
  - 42. Varma, M., Zisserman, A.: ‘A statistical approach to texture classification from single images’, Int. J. Comput. Vis., 2005, 62, (1–2), pp. 61–81 (doi: 10.1007/s11263-005-4635-4).
29. 29)
  - 19. Shen, L., Jia, S., Ji, Z., Chen, W.S.: ‘Extracting local texture features for image-based coin recognition’, IET Image Process., 2011, 5, (5), pp. 394–401 (doi: 10.1049/iet-ipr.2009.0251).
30. 30)
  - 4. Filiberto, P., Gema, G., Pedro, G.-S., Mirmehdi, M., Xianghua, X.: ‘Multi-spectral texture characterisation for remote sensing image segmentation’. Proc. of the Fourth Iberian Pattern Recognition and Image Analysis Conf., 2009.
31. 31)
  - 50. Mallat, S.: ‘A wavelet tour of signal processing’ (Elsevier Inc. San Diego, CA, 1999).
32. 32)
  - 53. Guoliang, F., Xiang-Gen, X.: ‘Wavelet-based texture analysis and synthesis using hidden Markov models’, IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 2003, 50, (1), pp. 106–120 (doi: 10.1109/TCSI.2002.807520).
33. 33)
  - 16. Enzweiler, M., Gavrila, D.M.: ‘A multilevel mixture-of-experts framework for pedestrian classification’, IEEE Trans. Image Process., 2011, 20, (10), pp. 2967–2979 (doi: 10.1109/TIP.2011.2142006).
34. 34)
  - 23. Qian, X., Hua, X.-S., Chen, P., Ke, L.: ‘PLBP: an effective local binary patterns texture descriptor with pyramid representation’, Pattern Recognit., 2011, 44, pp. 2502–2515 (doi: 10.1016/j.patcog.2011.03.029).
35. 35)
  - 44. Torralba, A., William, K., Freeman, T., Rubin, M.: ‘Context-based vision system for place and object recognition’ Proc. 9th IEEE Int. Conf. on Computer Vision, Nice, France, 2003, vol 1, pp. 273–280.
36. 36)
  - 20. Lazebnik, S., Schmid, C., Ponce, J.: ‘A sparse texture representation using local affine regions’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (8), pp. 1265–1278 (doi: 10.1109/TPAMI.2005.151).
37. 37)
  - 43. Varma, M., Zisserman, A.: ‘A statistical approach to material classification using image patch exemplars’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (11), pp. 2032–2047 (doi: 10.1109/TPAMI.2008.182).
38. 38)
  - 6. Fathi, A., Naghsh-Nilchi, A.: ‘Noise tolerant local binary pattern operator for efficient texture analysis’, Pattern Recognit. Lett., 2012, 33, (9), pp. 1093–1100 (doi: 10.1016/j.patrec.2012.01.017).
39. 39)
  - 38. Turtinen, M., Pietikäinen, M., Silvén, O.: ‘Visual characterization of paper using isomap and local binary patterns’, IEICE Trans., 2006, 89, pp. 2076–2083.
40. 40)
  - 20. Guoying, Z., Ahonen, T., Matas, J., Pietikainen, M.: ‘Rotation-invariant image and video description with local binary pattern features’, IEEE Transactions on Image Processing, 2012, 21, (4), pp. 1465–1477 (doi: 10.1109/TIP.2011.2175739).
41. 41)
  - 7. Caridade, C.M.R., Marçal, A.R.S., Mendonc, T.: ‘The use of texture for image classification of black & white air-photographs’, Int. J. Remote Sens., 2007, 29, (2), pp. 593–607 (doi: 10.1080/01431160701281015).
42. 42)
  - 12. Khellah, F.M.: ‘Texture classification using dominant neighborhood structure’, IEEE Trans. Image Process., 2011, 20, (11), pp. 3270–3279 (doi: 10.1109/TIP.2011.2143422).
43. 43)
  - 36. Li, L., Yunli, L., Fieguth, P.W., Songyang, L., Guoying, Z.: ‘BRINT: binary rotation invariant and noise tolerant texture classification’, IEEE Trans. Image Process., 2014, 23, (7), pp. 3071–3084 (doi: 10.1109/TIP.2014.2325777).
44. 44)
  - 27. Han, J., Ma, K.-K.: ‘Rotation-invariant and scale-invariant Gabor features for texture image retrieval’, Image Vis. Comput., 2007, 25, (9), pp. 1474–1481 (doi: 10.1016/j.imavis.2006.12.015).
45. 45)
  - 18. Su, S.Z., Chen, S.Y., Li, S.Z., Li, S.A., Duh, D.J.: ‘Structured local binary Haar pattern for pixel-based graphics retrieval’, Electron. Lett., 2010, 46, (14), pp. 996–998 (doi: 10.1049/el.2010.1104).
46. 46)
  - 54. Arasteh, S., Hung, C.-C., Kuo, B.-C.: ‘Image texture segmentation using local binary pattern and color information: a comparison’. Int. Computer Symp. (ICS 2006), 2006.
47. 47)
  - 52. Selvan, S., Ramakrishnan, S.: ‘SVD-based modeling for image texture classification using wavelet transformation’, IEEE Trans. Image Process., 2007, 16, (11), pp. 2688–2696 (doi: 10.1109/TIP.2007.908082).
48. 48)
  - 33. Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: ‘Computer vision using local binary patterns’ (Springer, London, 2011).
49. 49)
  - 14. Wang, W.W., Cao, Z.G.: ‘Recognition of blurred faces using local phase pattern’, Electron. Lett., 2012, 48, (20), pp. 1269–1271 (doi: 10.1049/el.2012.2482).
50. 50)
  - 58. Reyes-Aldasoro, C.C., Bhalerao, A.: ‘The Bhattacharyya space for feature selection and its application to texture segmentation’, Pattern Recognit., 2006, 39, (5), pp. 812–826 (doi: 10.1016/j.patcog.2005.12.003).
51. 51)
  - 28. Kittler, J.: ‘Handbook of pattern recognition and image processing’ (Academic Press, New York, NY, 1986).
52. 52)
  - 11. Guoying, Z., Pietikainen, M.: ‘Dynamic texture recognition using local binary patterns with an application to facial expressions’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (6), pp. 915–928 (doi: 10.1109/TPAMI.2007.1110).
53. 53)
  - 3. Liao, S., Law, M.W., Chung, A.C.: ‘Dominant local binary patterns for texture classification’, IEEE Trans. Image Process., 2009, 18, (5), pp. 1107–1118 (doi: 10.1109/TIP.2009.2015682).
54. 54)
  - 10. Baochang, Z., Yongsheng, G., Sanqiang, Z., Bineng, Z.: ‘Kernel similarity modeling of texture pattern flow for motion detection in complex background’, IEEE Trans. Circuits Syst. Video Technol., 2011, 21, (1), pp. 29–38 (doi: 10.1109/TCSVT.2011.2105591).
55. 55)
  - 32. Randen, T., Husoy, J.H.: ‘Filtering for texture classification: a comparative study’, IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21, (4), pp. 291–310 (doi: 10.1109/34.761261).
56. 56)
  - 27. Hand, D.J.: ‘Discrimination and classification’ (John Wiley & Sons, New York, NY, 1981).
57. 57)
  - 29. Available at http://www.outex.oulu.fi, accessed 2002.

Efficient levels of spatial pyramid representation for local binary patterns

References

Related content