ارائه روشی جدید از نگاشت توسعه‌یافته الگوی دودویی محلی جهت طبقه‌بندی تصاویر بافتی
محورهای موضوعی : مهندسی برق و کامپیوتر
محمدحسین شکور
1
,
فرشاد فرشاد تاجری‌پور
2
1 - دانشگاه آزاد اسلامي، واحد شيراز
2 - دانشگاه شیراز
کلید واژه: طبقه‌بندی بافت استخراج ویژگی الگوهای دودویی محلی الگوهای محلی همگن و ناهمگن,
چکیده مقاله :
طبقه‌بندی بافت از جمله شاخه‌های مهم پردازش تصویر است و مهم‌ترین نکته در طبقه‌بندی بافت‌ها، استخراج ویژگی‌های تصویر بافتی است. یکی از مهم‌ترین و ساده‌ترین روش‌ها، روش مبتنی بر الگوی دودویی محلی است که به دلیل سادگی در پیاده‌سازی و استخراج ویژگی‌های مناسب با دقت طبقه‌بندی بالا، مورد توجه قرار گرفته است. در اغلب روش‌های الگوی دودویی محلی بیشتر به الگوهای محلی همگن توجه شده و همه اطلاعات قسمت‌های ناهمگن تصویر صرفاً به عنوان یک ویژگی استخراج می‌شود. در این مقاله، یک شکل جدید از نگاشت الگوهای دودویی محلی ارائه شده که از اطلاعات الگوهای ناهمگن به شکل مناسب استفاده می‌کند. یعنی بر خلاف اغلب روش‌های قبلی، در اینجا از الگوهای محلی ناهمگن ویژگی‌های بیشتری استخراج می‌شود و در نتیجه دقت طبقه‌بندی بالاتر می‌رود. ضمن این که کلیه نکات مثبت روش‌های موجود مانند غیر حساس بودن به چرخش و تغییرات روشنایی را دارد. روش ارائه‌شده با استخراج ویژگی‌های بیشتر از الگوهای ناهمگن به دقت بالاتری از طبقه‌بندی نسبت به روش‌های مشهور و مهم دست یافته است. پیاده‌سازی روش ارائه‌شده روی پایگاه بافتی Outex این بهبود را نشان می‌دهد.
Texture classification is one of the important branches of image processing. The main point of texture classification is feature extraction. Local Binary Pattern (LBP) is one of the important methods that are used for texture feature extraction. This method is widely used because it has simple implementation and extracts high discriminative features from textures. Most of previous LBP methods used uniform patterns and only one feature is extracted from non-uniform patterns. In this paper, by extending non-uniform patterns a new mapping technique is proposed that extracts more discriminative features from non-uniform patterns. So in spite of almost all of the previous LBP methods, the proposed method extracts more discriminative features from non-uniform patterns and increases the classification accuracy of textures. The proposed method has all of the positive points of previous LBP variants. It is a rotation invariant and illumination invariant method and increase the classification accuracy. The implementation of proposed mapping on Outex dataset shows that proposed method can improve the accuracy of classifications significantly.
[1] M. Petrou and P. G. Sevilla, Image Processing Dealing with Texture, 2006.
[2] M. Tuceryan and A. K. Jain, Handbook of Pattern Recognition and Computer Vision, ch.2, pp. 207-248, World Scientific Publishing, 1998.
[3] R. M. Haralick, K. Shanmugam, and I. Dinestein, "Textural features for image classification," IEEE Trans. on Systems, Man, and Cybernetic, vol. 3, no. 6, pp. 610-621, Nov. 1973.
[4] D. Popescu, R. Dobrescu, and M. Nicolae, "Texture classification and defect detection by statistical features," International J. of Circuit, System, and Signal processing, vol. 1, no. 1, pp. 79-85, 2007.
[5] S. Hegenbarta and A. Uhla, "A scale - and orientation - adaptive extension of local binary patterns for texture classification," Pattern Recognition, vol. 48, no. 8, pp. 2633-2644, Aug. 2015.
[6] R. Davarzani, S. Mozaffari, and K. Yaghmaie, "Scale and rotation - invariant texture description with improved local binary pattern features," Signal Processing, vol. 111, pp. 274-293, Jun. 2015.
[7] Z. Li, G. Liu, Y. Yang, and J. You, "Scale-and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift," IEEE Trans. on Image Processing, vol. 12, no. 4, pp. 2130-2140, Apr. 2012.
[8] M. Pakdel and F. Tajeripour, "Texture classification using optimal Gabor filters," in Proc. First Int. Conf. on Computer and Knowledge Engineering, ICCKE'11, pp. 208-213, 13-14 Oct. 2011.
[9] X. Chen, X. Zeng, and D. van Alphen, "Multi-class feature selection for texture classification," Pattern Recognition Letters, vol. 27, no. 14, pp. 1685-1691, 15 Oct. 2006.
[10] M. Pietikainen, T. Ojala, and Z. Xu, "Rotation-invariant texture classification using feature distributions," Pattern Recognition, vol. 33, no. 1, pp. 43-52, Jan. 2000.
[11] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[12] S. Arivazhagan and L. Ganesan, "Texture classification using wavelet transform," Pattern Recognition Letters, vol. 24, no. 1-2, pp. 1513-1521, Jun. 2003.
[13] P. S. Hiremath and S. Shivashankar, "Texture classification using wavelet packet decomposition," Journal on Graphics, Vision and Image Processing., vol. 6, no. 2, pp. 77-80, Sep. 2006.
[14] B. V. Ramana Reddy, M. Radhika Mani, and K. V. Subbaivah, "Texture classification method using wavelet transform based on gaussian markov random field," International J. of Signal and Image processing, vol. 1, no. 1, pp. 35-39, Jan. 2010.
[15] V. Vijaya Kumar, U. S. N. Raju, M. Radhika Mani, and A. L. Narasimha Rao, "Wavelet based texture segmentation methods based on combinatorial of morphological and statistical operations," International J. of Computer Science and Network Security, vol. 8, no. 8, pp. 176-181, Aug. 2008.
[16] J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. on System, Man, and Cybernetic, vol. 6, no. 4, pp. 267-285, May 1976.
[17] T. Randen and J. H. Husoy, "Filtering for texture classification: a comparative study," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1996.
[18] T. Maenpaa, M. Pietikainen, and T. Ojala, "Texture classification by multi-predicate local binary pattern operators," in Proc. 15th Int. Conf. on Pattern Recognition, pp. 939-942, 3-7 Sep. 2000.
[19] M. Varma and A. Zisserman, "A statistical approach to texture classification from single images," International J. of Computer Vision, vol. 62, no. 1, pp. 61-81, Apr. 2005.
[20] M. Varma and A. Zisserman, "Texture classification: are filter banks necessary?" in Proc. Int. Conf. on Computer Vision and Pattern Recognition, pp. 691-698, 18-20 Jun. 2003.
[21] M. Varma and A. Zisserrman, "A statistical approach to material classification using image patch examplars," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2032-2047, Nov. 2009.
[22] F. Tajeripour, E. Kabir, and A. Sheikhi, "Fabric defect detection using modified local binary patterns," EURASIP J. on Advances in Signal Processing, vol. 2008, 12 pp., Jan. 2008.
[23] S. Soltaninejad, M. H. Shakoor, and F. Tajeripour, "Lung nodule segmentation based on modified local binary pattern," International J. of Scientific & Engineering Research, vol. 5, no. 12, pp. 575-586, Dec. 2014.
[24] F. Bianconi and A. Fernandez, "On the occurrence probability of local binary patterns: a theoretical study," J. of Mathematical Imaging and Vision Manuscript, vol. 40, no. 3, pp. 259-268, Jul. 2011.
[25] S. Liao, M. W. K. Law, and A. C. S. Chung, "Dominant local binary patterns for texture classification," IEEE Trans. on Image Processing, vol. 18, no. 5, pp. 1107-1118, Mar. 2009.
[26] Z. Guo, L. Zhang, and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE Trans. Image Process, vol. 9, no. 16, pp. 1657-1663, Jun. 2010.
[27] Z. Guo, L. Zhang, and D. Zhang, "Rotation invariant texture classification using LBP Variance (LBPV) with global matching," Pattern Recognition J., vol. 43, no. 3, pp. 706-719, Mar. 2010.
[28] Y. Zhao, D. S. Huang, and W. Jia, "Completed local binary count for rotation invariant texture classification," IEEE Trans. on Image Processing, vol. 21, no. 10, pp. 4492-4497, Oct. 2012.
[29] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE Trans. on Image Processing, vol. 19, no. 6, pp. 168-182, Jun. 2010.
[30] T. Ojala, et al., "Outex-a new framework for empirical evaluation of texture analysis algorithms," in Proc. of 16 Int. Conf. of Pattern Recognition, vol. 1, pp. 701-706, 11-15 Aug. 2002.
