A Completed Multi-Scale Local Statistics Pattern for Texture Classification
DOI:
https://doi.org/10.5566/ias.3037Keywords:
feature extraction, statistics encoding pattern, texture classification, local binary patternAbstract
Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features for texture classification. First, this paper encodes the center pixel, mean, variance and range of local neighborhood by corresponding multi-scale threshold, and proposes the local center pattern, local mean pattern, local variance pattern and local range pattern. Then, based on the compact multi-pattern encoding strategy, the four sub-patterns are jointly encoded in a 4-bit binary pattern, named as multi-scale local statistics pattern. Finally, for comprehensive texture representation, the multi-scale local statistics pattern is jointly combined with local sign pattern and local magnitude pattern to generate a completed multi-scale local statistics pattern for texture classification. Extensive experiments conducted on three representative databases demonstrate that the proposed completed multi-scale local statistics pattern achieves competitive classification performance compared with other state-of-the-art approaches.
References
Arya, R., & Vimina, E. R. (2021). Local Triangular Coded Pattern: A Texture Descriptor for Image Classification. IETE J Res (1): 1-12.
Bandzi, P., Oravec, M., & Pavlovicova, J. (2007). New Statistics for Texture Classification Based on Gabor Filters. Radioengineering 16(3): 133-7.
Banerjee, P., Bhunia, A. K., Bhattacharyya, A., et al. (2018). Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval. Expert Syst Appl 113(DEC.):100-15.
Bruna, J., & Mallat, S. (2013). Invariant scattering convolution networks. IEEE T Pattern Anal 35(8): 1872-86.
Cimpoi, M., Maji, S., Kokkinos, I., et al. (2014). Describing textures in the wild. Proceedings of CVPR 2014, Washington, DC, USA, 3606-13.
Cimpoi, M., Maji, S., & Vedaldi, A. (2015). Deep filter banks for texture recognition and segmentation. Proceedings of CVPR 2015, Boston, MA, USA, 3828-36.
Cohen, F. S., Fan, Z., & Patel, M. A. (1991). Classification of rotated and scaled textured images using Gaussian Markov random field models. IEEE T Pattern Anal 13(02): 192-202.
Cote, M., & Albu, A. B. (2015). Robust Texture Classification by Aggregating Pixel-Based LBP Statistics. IEEE Signal Proc Let 22(11): 2102-6.
Dana, K. J., Van Ginneken, B., Nayar, S. K., et al. (1999). Reflectance and texture of real-world surfaces. ACM T Graphic 18(1): 1-34.
Davis, L. S., Johns, S. A., & Aggarwal, J. K. (1979). Texture analysis using generalized co-occurrence matrices. IEEE T Pattern Anal 3: 251-9.
Faust, O., Acharya, U. R., Meiburger, K. M., et al. (2018). Comparative assessment of texture features for the identification of cancer in ultrasound images: A review. Biocybern Biomed Eng 38(2): 275-96.
Florindo, J. B., & Bruno, O. M. (2013). Texture analysis by multi-resolution fractal descriptors. Expert Syst Appl 40(10): 4022-8.
Florindo, J. B. (2020). DSTNet: Successive applications of the discrete Schroedinger transform for texture recognition. Inform Sciences 507: 356-64.
Florindo, J. B. (2024). Fractal pooling: A new strategy for texture recognition using convolutional neural networks. Expert Syst Appl 243: 122978.
Franklin, S. E., (2020). Interpretation and use of geomorphometry in remote sensing: a guide and review of integrated applications. Int J Remote Sens 41(19): 7700-33.
Ganesan, A., & Santhanam, S. M. (2021). Local Neighbourhood Edge Responsive Image Descriptor for Texture Classification Using Gaussian Mutated JAYA Optimization Algorithm. Arab J Sci Eng 46: 8151-70.
Guo Z, Zhang L, & Zhang D (2010a). A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE T Image Process 19(6): 1657-63.
Guo, Z., Zhang, L., & Zhang, D. (2010b). Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3): 706-19.
Iloanusi, O. N., & Ezema, C. A. (2017). A quantitative impact of fingerprint distortion on recognition performance. Lect Notes Comput Sc 26(6): 267-75.
Jain, L. C., Nanni, L., & Lumini, A. (2016).Local binary patterns: new variants and applications. Springer, NY.
Kim, N. C., & So, H. J. (2018). Directional statistical Gabor features for texture classification. Pattern Recogn Lett 112(SEP.1): 18-26.
Kwak, C., & Han, W. (2020). Towards Size of Scene in Auditory Scene Analysis: A Systematic Review. J Audiol Otol 24(1): 1-9.
Lewicki, M. S., Olshausen, B. A., Annemarie, S., et al. (2014). Scene analysis in the natural environment. Front Pharmacol 5:199.
Li, Y., Xu, X., Li, B., et al. (2018). Circular regional mean completed local binary pattern for texture classification. J Electron Imaging 27(4)
Liu, L., Chen, J., Fieguth, P., et al. (2019). From BoW to CNN: Two Decades of Texture Representation for Texture Classification. Int J Comput Vision 127(1): 74-109.
Liu, L., Fieguth. P. W., Hu, D., et al. (2015). Fusing Sorted Random Projections for Robust Texture and Material Classification. IEEE T Circ Syst Vid 25(3): 482-96.
Liu L, Fieguth P, M Pietikäinen, et al. (2016). Median Robust Extended Local Binary Pattern for Texture Classification. IEEE T Image Process 25(3): 1368-81.
Liu, L., Fieguth, P., Guo, Y., et al. (2017). Local binary features for texture classification: Taxonomy and experimental study. Pattern Recogn 62: 135-60.
Liu, L., Long, Y., Fieguth, P. W., et al (2014). BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification. IEEE T Image Process 23(7): 3071-84.
Liu, L., Zhao, L., Long, Y., et al. (2012). Extended local binary patterns for texture classification. Image Vision Comput 30(2): 86-99.
Manjunath, B. S., Ohm, J. R., Vasudevan, V. V., et al. (2001). Color and texture descriptors. IEEE T Circ Syst Vid 11(6): 703-15.
Mehta, R., & Egiazarian, K. (2016). Dominant Rotated Local Binary Patterns (DRLBP) for texture classification. Pattern Recogn Lett 71(Feb.1): 16-22.
Nguyen, T. P., Vu, N. S., & Manzanera, A. (2016). Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173(JAN.15PT.3): 1565-77.
Nguyen, V. D., Nguyen, D. D., Nguyen, T. T., et al. (2014). Support Local Pattern and Its Application to Disparity Improvement and Texture Classification. IEEE T Circ Syst Vid 24(2): 263-76.
Ojala T, Pietikainen M, & Maenpaa T (2002a). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE T Pattern Anal 24(7): 971-87.
Ojala, T., Maenpaa, T., Pietikainen, M., et al. (2002b). Outex - new framework for empirical evaluation of texture analysis algorithms. Proceedings of ICPR 2002, Quebec City, QC, Canada, 701-6.
Pan, Z., Fan, H., & Zhang, L. (2015). Texture Classification Using Local Pattern Based on Vector Quantization. IEEE T Image Process 24(12): 5379-88.
Pan, Z., Hu, S., Wu, X,, et al. (2021). Adaptive center pixel selection strategy to Local Binary Pattern for texture classification. Expert Syst Appl 180(4): 115123.
Pan Z, Li Z, Fan H, et al. (2017). Feature based local binary pattern for rotation invariant texture classification. Expert Syst Appl 88: 238-48.
Pan, Z., Wu, X., & Li, Z. (2019). Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification. Expert Syst Appl 120(APR.): 319-34.
Patino, J. E., & Duque, J. C. (2013). A review of regional science applications of satellite remote sensing in urban settings. Comput Environ Urban 37(1): 1-17.
Pothos, V. K., Theoharatos, C., Zygouris, E., et al. (2008). Distributional-based texture classification using non-parametric statistics. Pattern Anal Appl 11(2): 117-29.
Qi, X., Zhao, G., Shen, L., et al. (2016). LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification. Neurocomputing 184(apr.5):28-35.
Shakoor, M. H., & Boostani, R. (2017). Extended Mapping Local Binary Pattern Operator for Texture Classification. Int J Pattern Recogn 31(6): 1-22.
Silva, P. M., & Florindo, J. B. (2019). A statistical descriptor for texture images based on the box counting fractal dimension. Physica A 528(15): 121469.
Song, T., Feng, J., Wang, S., et al. (2020). Spatially weighted order binary pattern for color texture classification. Expert Syst Appl 147: 113167.
Song K, Yan Y, Zhao Y, et al. (2015). Adjacent evaluation of local binary pattern for texture classification. J Vis Commun Image R 33: 323-39.
Tan X, & Triggs B (2010). Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE T Image Process 19(6):1635-50.
Tuncer, T., Dogan, S., & Ertam, F. (2019). A novel neural network-based image descriptor for texture classification. Physica A, 526: 120955.
Unser, M. (1995). Texture classification and segmentation using wavelet frames. IEEE T Image Process 4(11), 1549-60.
Van der Meer, F. (2012). Remote-sensing image analysis and geostatistics. Int J Remote Sens 33(18): 5644-76.
Verma, M., & Raman, B. (2018). Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval. Multimed Tools Appl 77(10): 11843-66.
Varma, M., & Zisserman, A. (2005). A statistical approach to texture classification from single images. Int J Comput Vision 62(1–2): 61–81.
Wang, K., Bichot, C. E., Li, Y., et al. (2017). Local binary circumferential and radial derivative pattern for texture classification. Pattern Recogn 67: 213-29.
Wang, T., Dong, Y., Yang, C., et al. (2018). Jumping and Refined Local Pattern for Texture Classification. IEEE Access, 6: 64416-25.
Wang, S., Han, K., & Jin, J. (2019). Review of image low-level feature extraction methods for content-based image retrieval. Sensor Rev 39(6): 783-809.
Xu, Y., Ji, H., & Fermuller, C. (2006). A Projective Invariant for Textures. Proceedings of CVPR 2006, New York, NY, USA, 1932-9.
Xu, X., Li, Y., & Wu, Q. J. (2020a). A Multiscale Hierarchical Threshold-Based Completed Local Entropy Binary Pattern for Texture Classification. Cogn Comput 12(1): 224-37.
Xu, X., Li, Y., & Wu, Q. J. (2020b). A completed local shrinkage pattern for texture classification. Appl Soft Comput 97: 106830.
Xu, X., Li, Y., & Wu, Q. J. (2021). A compact multi-pattern encoding descriptor for texture classification. Digit Signal Process 114(2): 103081.
Yu, H., Yang, W., Xia, G. S., et al. (2016). A color-texture-structure descriptor for high-resolution satellite image classification. Remote Sens-Basel 8(3): 259.
Zhang, W., Zhang, W., Liu, K., et al. (2017a). A Feature Descriptor Based on Local Normalized Difference for Real-World Texture Classification. IEEE T Multimedia 20(4): 880-8.
Zhang, Z., Liu, S., Mei, X., et al. (2017b). Learning completed discriminative local features for texture classification. Pattern Recogn 67: 263-75.
Zhao, Y., Huang, D. S., & Jia, W. (2012). Completed Local Binary Count for Rotation Invariant Texture Classification. IEEE T Image Process 21(10): 4492-7.
Zhao, Y., Wang, R. G., Wang, W. M., et al. (2016). Local Quantization Code Histogram for Texture Classification. Neurocomputing 207(26): 354-64.
Zhao, Y., Jia, W., Hu, R. X., et al (2013). Completed robust local binary pattern for texture classification. Neurocomputing 106(15): 68-76.
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Copyright (c) 2024 Xiaochun Xu, Bin Li, Q.M. Jonathan Wu
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