بازیابی سریع تصاویر بافتی با استفاده از تبدیل والش- هادامارد

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسنده

استادیار،گروه مهندسی کامپیوتر، دانشگاه فنی و حرفه‌ای، تهران، ایران.

چکیده

یکی از مسائل مهم در پردازش تصویر، پیدا کردن الگوریتم‌هایی است که بتوان تشابه و عدم‌تشابه یک تصویر بافت را با سایر تصاویر در زمان کوتاه مشخص کند. از آنجایی که تصاویر بافت دارای الگوهای تکرارشونده در کل تصویر هستند، الگوریتم‌های مشابهت‌یابی برای تصاویر معمولی برای تصاویر بافت کارایی ندارند. الگوریتم‌های مبتنی بر یادگیری عمیق هم نیازمند حجم بسیار زیادی داده در همان گروه هستند و برای تصاویر بافت که حجم داده زیادی در دسترس نباشد کارایی ندارند. در این مقاله، الگوریتمی برای جستجوی سریع تشابه بافتی با استفاده از تبدیل والش- هادامارد، توسعه داده شد. این الگوریتم، از سه مرحله تشکیل شده است: در مرحله اول از فیلتر گابور برای استخراج بردارهایی با ابعاد بالا از هر بافت، استفاده شد. سپس، از یک تبدیل تصادفی‌شده والش- هادامارد استفاده گردید تا بردارهایی با ابعاد بالا به بردارهایی با دو بُعد جای‌گذاری شود. در مرحله سوم، از یک الگوریتم تقریب برای اندازه‌گیری فاصله بین دو بردار استفاده شد تا تشابه یا عدم‌تشابه بین دو تصویر بافت مشخص شود. نتایج آزمایش عملی الگوریتم گواه این امر هستند که این تقریب برای بافت‌های واقعی نسبتاً مناسب هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Fast Texture Retrieval Using Walsh-Hadamard Transform

نویسنده [English]

  • Mohammad Amiri
Assistant Professor, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.
چکیده [English]

One of the most important issues in image processing is to find algorithms that can determine the similarity and dissimilarity of a texture image with other images in a short time. Because texture images have repetitive patterns throughout the image, similarity algorithms for natural or non- texture images are not effective for texture images. Deep learning algorithms also require large amounts of data in the same group and for texture images that do not have much data volumes available, they do not work. In this paper, an algorithm was developed to rapidly search for tissue similarity using the Walsh-Hadamard transform. This algorithm consists of three steps. In the first step, the Gabor filter was used to extract the high-dimensional feature from each texture. Then, a randomized Walsh-Hadamard transform was used to convert high-dimensional feature from each texture into two-dimensional feature. In the third step, an earth mover distance (EMD) approximation algorithm was used to determine the similarity or dissimilarity between two textures that are represented by two-dimensional vectors. The results of the proposed algorithm proved that this approximation algorithm is relatively suitable for real tissues.

کلیدواژه‌ها [English]

  • Texture similarity Earth mover distance Walsh
  • Hadamard transform
[1] DeCost, B. L., Francis, T., & Holm, E. A. (2017). Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures. Acta Materialia, 133, 30-40. https://doi.org/10.1016/j.actamat.2017.05.014
[2] Farwell, L. S., Gudex-Cross, D., Anise, I. E., Bosch, M. J., Olah, A. M., Radeloff, V. C., Razenkova, E., Rogova, N., Silveira, E. M. O., Smith, M. M., & Pidgeon, A. M. (2021). Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness. Remote Sensing of Environment, 253, 112175. https://doi.org/10.1016/j.rse.2020.112175
[3] Hoang, N.-D. (2019). Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automation in Construction, 105, 102843. https://doi.org/10.1016/j.autcon.2019.102843
[4] Grauman, K., & Darrell, T. (2007). The Pyramid Match Kernel: Efficient Learning with Sets of Features. Journal of Machine Learning Research ,8 ,725-760 .
https://dl.acm.org/doi/10.5555/1248659.1248685
[5] Johnson, W. B. (1984). Extensions of Lipschitz mappings into Hilbert space. Contemporary mathematics, 26, 189-206. https://doi.org/10.1090/conm/026/737400
[6] Veerashetty, S., & Patil, N. (2019) .Design of rotation, illumination, and scale invariant Gabor texture descriptor for image texture analysis and retrieval. International Journal of Computers and Applications, 43, 1-9. https://doi.org/10.1080/1206212X.2019.1658378
[7] Sahib, Z. A., Uçan, O. N., Talab, M. A., Alnaseeri, M. T., Mohammed, A. H., & Sahib, H. A. (2020, June 26-28 ). Hybrid Method Using EDMS & Gabor for Shape and Texture. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) ,Ankara, Turkey. https:// ieeexplore.ieee.org/abstract/document/9152829 /
[8] Kim, N. C., & So, H. J. (2018). Directional statistical Gabor features for texture classification. Pattern Recognition Letters, 112, 18-26. https://doi.org/10.1016/j. patrec.2018.05.010
[9] Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
[10] Liu, P., Guo, J.-M., Chamnongthai, K., & Prasetyo, H. (2017). Fusion of color histogram and LBP-based features for texture image retrieval and classification. Information Sciences, 390, 95-111. https://doi.org/10.1016/j.ins.2017.01.025
[11] Garg, M., & Dhiman, G. (2021). A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing and Applications, 33(4), 1311-1328. https://doi.org/10.1007/s00521-020-05017-z
[12] Yuan, X., Yu, J., Qin, Z., & Wan, T. (2011). A SIFT-LBP image retrieval model based on bag of features. 2011 18th IEEE International Conference on Image Processing, https://www.semanticscholar.org/paper/A-SIFT-LBP-IMAGE-RETRIEVAL-MODEL-BASED-ON-Yuan-Yu/a90bf09e362b7902d68e37aee6389f2a16e3aa47
[13] Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, 93(26), 429-441 .
[14] Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A, 2(7), 1160-1169. https://doi.org/10.1364 /JOSAA.2.001160
[15] Marcelja, S. (1980). Mathematical description of the responses of simple cortical cells. J Opt Soc Am, 70(11), 1297-1300. https://doi.org/10.1364/josa.70.001297
[16] Kassis, M., & El-Sana, J. (2016, Oct 23-26 ). Scribble Based Interactive Page Layout Segmentation Using Gabor Filter. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, China. https:// ieeexplore.ieee.org/abstract/document/7814032
[17] Li, C., Huang, Y., & Zhu, L. (2017). Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recognition, 64, 118-129. https://doi.org/10.1016/j.patcog.2016.10.030
[18] Madhavi, D., & Patnaik, M. R. (2018). Genetic algorithm-based optimized gabor filters for content-based image retrieval. In Intelligent communication, control and devices (pp. 157-164). Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_18
[19] Patil, J. K., & Kumar, R. (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10(2), 69-78. https://doi.org/10.1016/j.eaef.2016.11.004
[20] Sahib, Z. A., Uçan, O. N., Talab, M. A., Alnaseeri, M. T., Mohammed, A. H., & Sahib, H. A. (2020 ,26-28 June 2020). Hybrid Method Using EDMS & Gabor for Shape and Texture. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) ,
[21] Samantaray, A. K., & Rahulkar, A. D. (2020). New design of adaptive Gabor wavelet filter bank for medical image retrieval. IET Image Processing, 14(4), 679-687. https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1024
[22] Singh, J., Bajaj, A., Mittal, A., Khanna, A., & Karwayun, R. (2018, Dec 14-15 ) .
Content Based Image Retrieval using Gabor Filters and Color Coherence Vector. 2018 IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India https://ieeexplore.ieee.org/abstract/document/8692123
[23] Feldman, D., Monemizadeh, M., & Sohler, C. (2007, June 6-8 ). A PTAS for k-means clustering based on weak coresets. Proceedings of the twenty-third annual symposium on Computational geometry, Gyeongju South Korea. https://dl.acm. org/doi/abs/10.1145/1247069.1247072
[24] Arriaga ,R. I., & Vempala, S. (2006). An algorithmic theory of learning: Robust concepts and random projection. Machine learning, 63(2), 161-182. https://doi.org/10.1109/SFFCS.1999.814637
[25] Dasgupta, A., Kumar, R., & Sarlós, T. (2010, June 5-8). A sparse johnson: Lindenstrauss transform. Proceedings of the forty-second ACM symposium on Theory of computing, Ithaca, New York. https://dl.acm.org/doi/abs/10.1145/ 1806689.1806737
[26] Clarkson, K. L., & Woodruff, D. P. (2017). Low-rank approximation and regression in input sparsity time. Journal of the ACM (JACM), 63(6), 1-45. https://doi.org/doi. org/10.1145/3019134
[27] Alon, N., Matias, Y., & Szegedy, M. (1999). The Space Complexity of Approximating the Frequency Moments. Journal of Computer and System Sciences, 58(1), 137-147. https://doi.org/10.1006/jcss.1997.1545
[28] Ailon, N., & Chazelle, B. (2009). The fast Johnson–Lindenstrauss transform and approximate nearest neighbors. SIAM Journal on computing, 39(1), 302-322. https://doi.org/10.1137/060673096
[29] Abdelmounaime, S., & Dong-Chen, H. (2013). New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis. ISRN Machine Vision, 2013, 876386. https://doi.org/10.1155/2013/876386
[30] Sotoodeh, M., Moosavi, M., & Boostani, R. (2019) .A Novel Adaptive LBP-Based Descriptor for Color Image Retrieval. Expert systems with Applications, 127, 342-352. https://doi.org/10.1016/j.eswa.2019.03.020
[31] Dolly, B., & Raj, D. (2021). Texture Based Image Retrieval Using GLCM and LBP. In International Conference on Intelligent and Smart Computing in Data Analytics: ISCDA 2020 (pp. 35-45). Springer Singapore. https://doi.org/10.1007/978-981-33-6176-8_5
[32] Doshi, N. P., & Schaefer, G. (2012, Nov 11-15 ). A comprehensive benchmark of local binary pattern algorithms for texture retrieval. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan https://ieeexplore.ieee.org/abstract/document/6460737
[33] Matoušek, J. (2008). On variants of the Johnson–Lindenstrauss lemma. Random Structures & Algorithms, 33(2), 142-156. https://doi.org/10.1002/rsa.20218
[34] Arora, S. (1998). Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems. Journal of the ACM (JACM), 45(5) ,753-
782 .https://doi.org/10.1145/290179.290180
[35] Charikar, M. S. (2002, May). Similarity estimation techniques from rounding algorithms. Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, Montreal, Quebec, Canada. https://doi.org/10.1145/509907.509965
[36] Grauman, K., & Darrell, T. (2005, Oct 17-21 ). The pyramid match kernel: discriminative classification with sets of image features. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, Beijing, China. https://ieeexplore.ieee.org/abstract/document/1544890