Abstract
In case of illumination change, the local binary pattern (LBP) descriptor have found to be used in analysis of texture of the image because of the ease of computation and robustness to such changes. However, the LBP technique also comes with limitations such as its inability to capture the discriminative information completely. For enhancing the LBP’s performance, we proposed a new texture descriptor for rotation, illumination and scale invariance (IRSLBP) for texture classification. The proposed approach extracts the color features through quantification of the RGB space into single channel, which is marked by a smaller number of shades to reduce computation and to improve the efficiency. The IRSLBP descriptor provides the scale invariance by considering the circular neighbor set of every central pixel other than the normal neighboring pixels. Moreover, the proposed IRSLBP decomposed the difference vector into sign part and magnitude part by local difference sign magnitude transform. In addition, these mitigated the influence of rotation, illumination or noise and demonstrated effective robustness. Using the proposed IRSLBP descriptor, we have classified the different textures using Multi kernel support vector machine (SVM) approach.
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References
Di Ruberto C (2017) Histogram of radom transform and text on matrix for texture analysis and classification. IET Image Process 11(9):760–766
Virupakshappa, Amrapur DB (2018) Computer based diagnosis system for tumor detection & Classification: a hybrid approach. International Journal of Pure and Applied Mathematics 118(7):33–43
Veerashetty S, Patil DNB (2018) HEp-2 cell image classification by zigzag ordering algorithm for clinical pathology test. International Journal of Pure and Applied Mathematics 118(9):711–716
Mehta R, Egiazarian K (2016) Rotation invariant texture description using symmetric dense microblock difference. IEEE Signal Process Lett 23(6):833–837
Susan S, Hanmandlu M (2013) Difference theoretic feature set for scale-, illumination-and rotation-invariant texture classification. IET Image Process 7(8):725–732
Candemir S, Borovikov E, Santosh KC, Antani S, Thoma G (2015) Rsilc: rotation-and scale-invariant, line-based color-aware descriptor. Image Vis Comput 42:1–12
Yang Y, Duan F, Ma L, Jiang J (2018) A Robust method for constructing rotational invariant descriptors. Signal Process Image Commun 60:224–236
Virupakshappa, Basavaraj A (2019) Health Technol. https://doi.org/10.1007/s12553-018-00288-y
Ambika, Biradar RL (2019) Health Technol. https://doi.org/10.1007/s12553-018-00289-x
Tao G, Zhao X, Chen T, Liu Z, Li S (2017) Illumination-insensitive image representation via synergistic weighted center-surround receptive field model and weber law. Pattern Recogn 69:124–140
Sandid F, Douik A (2016) Robust color texture descriptor for material recognition. Pattern Recogn Lett 80:15–23
Citraro L, Mahmoodi S, Darekar A, Vollmer B (2017) Extended three-dimensional rotation invariant local binary patterns. Image Vis Comput 62:8–18
Veerashetty S, Dr Patil NB (2018) Robust approach for texture analysis using radon and PCET descriptor. Journal of Advanced Research in Dynamical and Control Systems 10, 12-Special Issue
Kaddar B, Fizazi H, Boudraa A-O (2017). Texture features based on an efficient local binary pattern descriptor. Comput Electr Eng
Pan Z, Li Z, Fan H, Wu X (2017) Feature based local binary pattern for rotation invariant texture classification. Expert Systems with Applications 88:238–248
Li L, Zhao L, Long Y, Kuang G, Fieguth P (2012) Extended local binary patterns for texture classification. Image Vis Comput 30(2):86–99
Salwa L, Mohammed R (2017) Novel phase-based descriptor using bispectrum for texture classification. Pattern Recogn Lett 100:1–5
Hao Y, Li S, Mo H, Li H (2018) Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classification." In International Conference on Image and Graphics, pp. 199-210. Springer, Cham, Antić, Aco, BranislavPopović, LidijaKrstanović, RatkoObradović, and MijodragMilošević. "Novel texture-based descriptors for tool wear condition monitoring. Mech Syst Signal Process 98: 1–15
Pan Z, Li Z, Fan H, Wu X (2017) Feature based local binary pattern for rotation invariant texture classification. Expert Systems with Applications 88:238–248
Calzada-Ledesma V, Puga-Soberanes HJ, Rojas-Domínguez A, Ornelas-Rodriguez M, Carpio M, Gómez CG (2018) A comparison of image texture descriptors for pattern classification." In fuzzy logic augmentation of neural and optimization algorithms: theoretical aspects and real applications, pp 515–525. Springer, Cham
Singh C, Walia E, Kaur KP (2018) Color texture description with novel local binary patterns for effective image retrieval. Pattern Recogn 76:50–68
Guo Z, Wang X, Zhou J, You J (2016) Robust texture image representation by scale selective local binary patterns. IEEE Trans Image Process 25(2):687–699
Liu G-H, Zhang L, Hou Y-K, Li Z-Y, Yang J-Y Image retrieval based on multitexton histogram. Pattern Recogn 43(7):2380–2389
Virupakshappa, Amarapur B (2018) Cogn Tech Work. https://doi.org/10.1007/s10111-018-0472-4
Veerashetty S, Patil NB (2017) Texture feature extraction based on multichannel decoded local binary pattern. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, pp 1173–1177. https://doi.org/10.1109/CTCEEC.2017.8455138
Virupakshappa, Amarapur B (2018) Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6176-1
Antić A, Popović B, Krstanović L, Obradović R, Milošević M (2018) Novel texturebased descriptors for tool wear condition monitoring. Mech Syst Signal Process 98:1–15
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Veerashetty, S., Patil, N.B. Novel LBP based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel SVM. Multimed Tools Appl 79, 9935–9955 (2020). https://doi.org/10.1007/s11042-019-7345-6
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DOI: https://doi.org/10.1007/s11042-019-7345-6