New Weighted Mean-Based Patterns for Texture Analysis and Classification

References

• Adnan, S., A. Irtaza, S. Aziz, M. Ullah, A. Javed, and M. Tariq. 2018. Fall detection through acoustic local ternary patterns. Applied Acoustics 140: (296–300):296–300. doi:10.1016/j.apacoust.2018.06.013.
   Google Scholar
• Agarwal, M., A. Singhal, and B. Lall. 2018. 3D local ternary co-occurrence patterns for natural, texture, face and biomedical image retrirval. Neurocomputing 313:1–24. doi:10.1016/j.neucom.2018.06.027.
   Google Scholar
• Alfy, E., and A. Binsaadoon. 2017. Silhouette-based gender recognition in smart environments using fuzzy local binary patterns and support vector machines. Procedia Computer Science 109: (164–171). doi: 10.1016/j.procs.2017.05.313.
   Google Scholar
• Aparicio, A. G. L., R. V. Monedero, and K. Engan. 2018. Noise robust and rotation invariant framework for texture analysis and classification. Applied Mathematics and Computation 335: (124–132). doi: 10.1016/j.amc.2018.04.018.
   Google Scholar
• Attia, A., Z. Akhtar, N. E. Chalabi, S. Maza, Y. Chahir. 2020. Deep rule-based classifier for finger knuckle pattern recognition system. Evolving Systems 1:1-15. doi: 10.1007/s12530-020-09359-w.
   Google Scholar
• Basu, S., S. Mukhopadhyay, M. Karki, R. Dibiano, S. Ganguly, R. Nemani, and S. Gayaka. 2017. Deep neural networks for texture classification- A theoretical analysis. Neural Networks 1–28. doi:10.1016/j.neunet.2017.10.001.
   Google Scholar
• Bianconi, F., A. Fernandez, E. Gonzalez, D. Caride, and A. Calvino. 2009. Rotation-invariant colour texture classification through multilayer CCR. Pattern Recognition Letters 30 ((8):):765–73. doi:10.1016/j.patrec.2009.02.006.
   Web of Science ®Google Scholar
• Brancati, N., G. Pietro, M. Frucci, and L. Gallo. 2016. Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering. Computer Vision and Image Understanding 155: (1–16). doi: 10.1016/j.cviu.2016.12.001.
   PubMedGoogle Scholar
• Busch, A., and W. Boles. 2002. Texture classification using multiple wavelet analysis. Digital Image Computing Techniques and Applications 1–8. Melbourne, Australia.
   Google Scholar
• Cai, J., F. Xing, A. Batra, F. Liu, G. A. Walter, K. Vandenborne, and L. Yang. 2019. Texture analysis for muscular dytrophy classification in MRI with improved class activation mapping. Pattern Recognition 86: (368–375):368–75. doi:10.1016/j.patcog.2018.08.012.
   PubMedGoogle Scholar
• Campana, B. J. L., and E. J. Keogh. 2010. A compression-based distance measure for texture. Statistical Analysis and Data Mining 3 (6):381–98. doi:10.1002/sam.10093.
   Google Scholar
• Chalechale, A., A. Mertins, and G. Naghdi. 2004. Edge image description using angular radial partitioning. IEE Proceeding, Vision, Image and Signal Processing 151 ((2):):93–101. doi:10.1049/ip-vis:20040332.
   Google Scholar
• Chen, J., S. Shan, C. He, G. Zhao, M. Pietikainen, X. Chen, and W. Gao. 2010. WLD: A robust local image descriptor. IEEE Transaction on Pattern Analysis and Machine Inteligence 32 ((9):):1705–20. doi:10.1109/TPAMI.2009.155.
   PubMed Web of Science ®Google Scholar
• Condori, R. H. M., and O. M. Bruno. 2020. Analysis of activation maps through global pooling measurements for texture classification. Information Sciences 1–32. doi:10.1016/j.ins.2020.09.058.
   Web of Science ®Google Scholar
• Cruz, L. B. D., J. C. Souza, J. A. D. Sousa, A. M. Santos, A. C. D. Paiva, J. D. S. D. Almeida, A. C. Silva, G. B. Junior, and M. Gattass. 2019. Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis. Computer Methods and Programs in Biomedicine 1–28. doi:10.1016/j.cmpb.2019.105269.
   Web of Science ®Google Scholar
• Dan, Z., Y. Chen, Z. Yang, and G. Wu. 2014. An Improved local binary pattern for texture classification. Optik 125 ((20):):6320–24. doi:10.1016/j.ijleo.2014.08.003.
   Web of Science ®Google Scholar
• Ershah, S., and F. Tajeripour. 2017. Multi-resolution and noise-resistant surface defect detection approach using new version of local binary patterns. Applied Artificial Intelligence 31: (1–10). doi: 10.1080/08839514.2017.1378012.
   Google Scholar
• Ferrer, M., C. Travieso, and J. Alonso. 2005. Using hand knuckle texture for biometric identification. In: Proceedings 39th annual international carnahan conference on security technology, (pp. 74–78) Las Palmas, Spain.
   Google Scholar
• Gao, G., J. Yang, J. Qian, and L. Zhang. 2014. Integration of multiple prientation for finger knuckle print verification. Neurocomputing 135: (180–191):180–91. doi:10.1016/j.neucom.2013.12.036.
   Google Scholar
• Guha, T., and R. K. Ward. 2014. Image similarity using sparse representation and compression distanse. IEEE Transaction on Multimedia 16 ((4):):980–87. doi:10.1109/TMM.2014.2306175.
   Web of Science ®Google Scholar
• Guo, J., H. Prasetyo, and H. Su. 2013. Image indexing using the color and bit pattern feature fusion. Journal of Vision Communication and Image Representation 24 ((8):):1360–79. doi:10.1016/j.jvcir.2013.09.005.
   Web of Science ®Google Scholar
• Guo, Z., L. Zhang, and D. Zhang. 2010. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition 43 ((3):):706–19. doi:10.1016/j.patcog.2009.08.017.
   Web of Science ®Google Scholar
• Hafiane, A., K. Palaniappan, and G. Seetharaman. 2015. Joint adaptive median binary patterns for texture classification. Pattern Recognition 48 ((8):):1–12. doi:10.1016/j.patcog.2015.02.007.
   Web of Science ®Google Scholar
• Heidari, H., and A. Chalechale. 2016. An evolutionary stochastic approach for efficient image retrieval using modified particle swarm optimization. International Journal of Advanced Computer Science and Applications 7 (7):105–12. doi:10.14569/IJACSA.2016.070715.
   Web of Science ®Google Scholar
• Heidari, H., and A. Chalechale. 2020. A new biometric identity recognition system based on a combination of superior features in finger knuckle print images. Turkish Journal of Electrical Engineering & Computer Science 28: (238–252). doi: 10.3906/elk-1906-12.
   Google Scholar
• Hiremath, P. S., and R. A. Bhusnurmath. 2017. Multiresolution LDBP descriptors for texture classification using anisotropic diffusion with an application to wood texture analysis. Pattern Recognition Letters 17: (1–10). doi: 10.1016/j.patrec.2017.01.015.
   Google Scholar
• Hoang, M. A., J. M. Geusebroek, and A. W. M. Smeulders. 2005. Color texture measurement and segmentation. Signal Processing 85 ((2):):265–75. doi:10.1016/j.sigpro.2004.10.009.
   Web of Science ®Google Scholar
• Jayech, K., M. A. Mahjoub, and N. E. B. Amara. 2016. Synchronous multi-stream hidden Markov modal for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing 214: (958–971):958–71. doi:10.1016/j.neucom.2016.07.020.
   Google Scholar
• Ji, G., K. Li, G. Zhang, S. Li, and L. Zhang. 2019. An assessment method for shale fracability based on fractal theory and fracture toughness. Engineering Fracture Mechanics 211: (282–290):282–90. doi:10.1016/j.engfracmech.2019.02.011.
   Google Scholar
• Ji, L., Y. Ren, X. Pu, and G. Liu. 2018. Median local ternary patterns optimied with rotation invariant uniform three mapping for noisy texture classification. Pattern Recognition 79: (1–40):387–401. doi:10.1016/j.patcog.2018.02.009.
   Google Scholar
• Joo, H., and J. Jeon. 2015. Feature-point extraction based on an improved SIFT algorithm. Control, Automation and Systems, (pp. 1–5) Jeju, South Korea.
   Google Scholar
• Jung, H. 2017. Analysis of reduced-set construction using image reconstruction from a HOG feature vector. IET Computer Vision 11 ((8):):725–32. doi:10.1049/iet-cvi.2016.0317.
   Web of Science ®Google Scholar
• Junior, J. J., P. C. Cortex, and A. R. Backes. 2014. Color texture classification using shortest paths in graphs. IEEE Transactions on Image Processing 23 ((9):):3751–61. doi:10.1109/TIP.2014.2333655.
   PubMed Web of Science ®Google Scholar
• Khatami, A., M. Babaie, H. Tizhoosh, A. Khosravi, T. Nguyen, and S. Nahavandi. 2018. A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval. Expert Systems with Application 100: (224–233):224–33. doi:10.1016/j.eswa.2018.01.056.
   Google Scholar
• Kokare, M., P. Biswas, and B. Chatterji. 2005. Texture image retrieval using new rotated complex wavelet filters. IEEE Transactions on System Management Cybern 35 ((6):):1168–78. doi:10.1109/TSMCB.2005.850176.
   PubMed Web of Science ®Google Scholar
• Kwitt, R., and A. Uhl. 2008. Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval. IEEE International Conference on Image Processing, (pp. 933–36) San Diego, CA, USA.
   Google Scholar
• Kwitt, R., and A. Uhl. 2010. Lightweight probabilistic texture retrieval. IEEE Transactions on Image Processing 19 ((1):):241–53. doi:10.1109/TIP.2009.2032313.
   PubMed Web of Science ®Google Scholar
• Kylberg, G., and I. Sintorn. 2013. Evaluation of noise robustness for local binary pattern descriptors in texture classification. Journal on Image and Video Processing 17:1–20.
   Google Scholar
• Lasmar, N., and Y. Berthoumieu. 2014.Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms. IEEE Transactions on Image Processing 23 ((5):):2246–61. doi:10.1109/TIP.2014.2313232.
   PubMed Web of Science ®Google Scholar
• Li, C., Y. Huang, and L. Zhu. 2016. Color texture image retrieval based on Gaussian copula models of Gabor wavelet. Pattern Recognition 64: (1–16). doi: 10.1016/j.patcog.2016.10.030.
   Google Scholar
• Lippi, M., S. Gianotti, A. Fama, M. Casali, E. Barbolini, A. Ferrari, F. Fioroni, M. Iori, S. Luminari, M. Menga, et al. 2019. Texture analysis and multiple-instance learning for the classification of malignant lymphomas. Computer Methods and Programs in Biomedicine 124–32. doi:10.1016/j.cmpb.2019.105153.
   Web of Science ®Google Scholar
• Liu, L., Y. Long, P. W. Fieguth, S. Lao, and G. Zhao. 2014. BRINT: Binary rotation invariant and noise tolerant texture classification. IEEE Transactions on Image Processing 23 ((7):):3071–84. doi:10.1109/TIP.2014.2325777.
   PubMed Web of Science ®Google Scholar
• Liu, G., H.,Z. Lei, Y. Xu.. 2011.Image retrieval based on micro-structure descriptor. Pattern Recognition Letters 44(9):2123–2133. doi:10.1016/j.patcog.2011.02.003.
   Google Scholar
• Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. Computer Vision 60 (2):91–110. doi:10.1023/B:VISI.0000029664.99615.94.
   Web of Science ®Google Scholar
• Manjunath, B., and W. Ma. 1996. Texture feature for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18 ((8):):837–42. doi:10.1109/34.531803.
   Web of Science ®Google Scholar
• Meng, F., M. Song, B. Guo, R. Shi, and D. Shan. 2016. Image fusion based on object region detection and non-subsampled contourlet transform. Computers & Electrical Engineering 62: (1–9). doi: 10.1016/j.compeleceng.2016.09.019.
   Google Scholar
• Murala, S., R. Maheshwari, and R. Balasubramanian. 2012. Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing 21 ((5):):2874–86. doi:10.1109/TIP.2012.2188809.
   PubMed Web of Science ®Google Scholar
• Nanni, L., S. Brahnam, and A. Lumini. 2010. Selecting the best performing rotation invariant patterns in local binary/ternary patterns. International Conference on IP, Computer Vision and Pattern Recognition, (pp. 369–75) USA.
   Google Scholar
• Nasser, M. A., J. Melendez, A. Moreno, O. A. Omer, and D. Puig. 2017. Breast tumor classificaation in ultrasound images using texture analysis and super-resolution methods. Engineering Applications of Artificial Intelligence 59: (84–92). doi: 10.1016/j.engappai.2016.12.019.
   Google Scholar
• Pan, Z., Z. Li, H. Fan, and X. Wu. 2017. Feature based local binary pattern for rotation invariant texture classification. Expert Systems with Applications 88: (238–248):238–48. doi:10.1016/j.eswa.2017.07.007.
   Google Scholar
• Paschos, G., and M. Petrou. 2003. Histogram ratio features for color texture classifiction. Pattern Recognition Letters 24 ((1–3):):309–14. doi:10.1016/S0167-8655(02)00244-1.
   Web of Science ®Google Scholar
• Porebski, A., N. Vandenbroucke, and L. Macaire. 2008. Haralick feature extraction from LBP images for color texture classification. In Proceedings of the 1st Workshops on Image Processing Theory, Tools and Applications, (pp. 1–8) Sousse, Tunisia.
   Google Scholar
• Ramteke, R., and K. Monali. 2012. Automatic medical image classification and abnormality detection using K nearest neighbour. Advanced Computer Research 2:190–96.
   Google Scholar
• Rios, A. G., S. Tabik, J. Luengo, and A. S. M. Shihavuddin. 2019. Coral species identification with texture or structure images using a two-level classifier based on convolutional neural networks. Knowledge-Based Systems 184: (1–10). doi: 10.1016/j.knosys.2019.104891.
   Google Scholar
• Satpathy, A., X. Jiang, and H. L. Eng. 2014. LBP-based edge-texture features for object recognition. IEEE Transaction on Image Processing 23:1953-1964. doi:10.1109/TIP.2014.2310123.
   Google Scholar
• Singh, C., E. Walia, and K. Kaur. 2018. Color texture description with novel local binary patterns for effective image retrieval. Pattern Recognition 76: (50–68):50–68. doi:10.1016/j.patcog.2017.10.021.
   Google Scholar
• Song, S., Z. Li, L. Niu, X. Zhou, G. Wang, Y. Gao, J. Wang, F. Liu, Q. Sui, L. Jiao, et al. 2019. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: Preliminary data from arterial phase scans texture analysis for classification. Clinical Rediology 74: (653–671). doi: 10.1016/j.crad.2019.05.010.
   Google Scholar
• Srivastava, P., and A. Khare. 2017. Integration of wavelet transform, local binary patterns and moments for content based image retrival. Visual Comminication and Image Representation 42:78–103. doi:10.1016/j.jvcir.2016.11.008.
   Web of Science ®Google Scholar
• Subrahmanyam, M., R. Maheswari, and R. Balasubramanian. 2012. Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Processing 92 ((6):):1467–79. doi:10.1016/j.sigpro.2011.12.005.
   Web of Science ®Google Scholar
• Viji, K. S. A., and D. H. Rajesh. 2020. An efficient technique to segment the tumor and abnormality detection in the brain MRI images using KNN classifier. Material Today 24:1944–1954. doi: 10.1016/j.matpr.2020.03.622.
   Google Scholar
• Xikai, X., J. Dong, W. Wang, and T. Tan. 2015. Local correction pattern for image steganalysis. IEEE China Summit and International Conference on Signal and Information Processing(pp. 1–6) Chengdu, China
   Google Scholar
• Xu, Z., Y. Jiang, Y. Wang, Y. Zhou, Q. Liao, and Q. Liao. 2019. Local polynomial contrast binary patterns for face recognition. Neurocomputing 355: (1–12):1–12. doi:10.1016/j.neucom.2018.09.056.
   Google Scholar
• Yang, C., and Q. Yu. 2018. Multiscale fourier descriptor based on triangular features for shape retrieval. Signal Processing: Image Communication 71::1–44. doi:10.1016/j.image.2018.11.004.
   Web of Science ®Google Scholar
• Yang, C., and Y. Yang. 2017. Improved local binary pattern for real scene optical character recognition. Pattern Recognition Letters 100: (14–21):14–21. doi:10.1016/j.patrec.2017.08.005.
   Google Scholar
• Yuan, F., X. Xia, and J. Shi. 2018. Mixed co-occurrence of local binary patterns and hamming distance based local binary patterns. Information Sciences 460-461:202–22. doi:10.1016/j.ins.2018.05.033.
   Web of Science ®Google Scholar
• Zeinali, B., A. Ayatollahi, and M. Kakooei. 2014. A novel method of applying directional filter bank (DFB) for finger-knuckle-print (FKP) recognition. In: Electrical engineering, 22th iranian conference, (pp. 500–04) Tehran, Iran.
   Google Scholar
• Zhang, B., Y. Gao, S. Zhao, and J. Liu. 2010. Local derivate pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing 19 (2):533–44. doi:10.1109/TIP.2009.2035882.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., W. Jia, R. Hu, and H. Min. 2013. Completed robust local binary pattern for texture classification. Neurocomputing 106: (68–76):68–76. doi:10.1016/j.neucom.2012.10.017.
   Google Scholar