Abstract
In this research, we suggested a novel texture descriptor distance-based Adjacent Local Binary Pattern AdLBP based on the adjacent neighbor window and the relationships among the sequential neighbors pixel value with a given distance parameter. The suggested technique calculates the neighbor and extracts the binary code from the adjacent neighborhood window and surrounding sub-image window in order to improve the adjacent neighbor information and change the conventional LBP thresholding schema. Additionally, we expanded this adjacent distance-based local binary pattern AdLBP and combined it with the evaluation window-based local binary pattern EwLBP to create a texture descriptor for texture classification that is more robust texture descriptor against noise. Finally combine AdLBP And EwLBP using encoding strategy to propose an Evaluation window based on Adjacent Distance Local Binary Pattern EADLBP descriptor for Image Classification. These descriptors are tested with the KTH-TIPS, KTH-TIPS2b to the applicability of the proposed method. In comparison, the proposed EADLBP approach is more robust against noise and consistently out- perform all of the fundamental methods.
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References
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Zhou, H., Wang, R., Wang, C.: A novel extended local-binary-pattern operator for texture analysis. Inf. Sci. 178(22), 4314–4325 (2008)
Fathi, A., Naghsh-Nilchi, A.R.: Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recogn. Lett. 33(9), 1093–1100 (2012)
Zhao, Y., Jia, W., Hu, R.-X., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)
Kurniawardhani, A., Suciati, N., Arieshanti, I.: Klasifikasi citra batik menggunakan metode ekstraksi ciri yang invariant terhadap rotasi. Jurnal Ilmiah Teknologi Informasi 12(2), 48–60 (2014)
Minarno, A.E., Munarko, Y., Bimantoro, F., Kurniawardhani, A., Suciati, N.: Batik image retrieval based on enhanced micro-structure descriptor. In: 2014 Asia-Pacific Conference on Computer Aided System Engineering (APCASE), pp. 65–70. IEEE, (2014)
Kaya, Y., Ertuğrul, Ö.F., Tekin, R.: Two novel local binary pattern descriptors for texture analysis. Appl. Soft Comput. 34, 728–735 (2015)
Chaki, J., Dey, N.: Texture Feature Extraction Techniques for Image Recognition. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0853-0
Islam, M.A., Uddin, M.A., Lee, Y.K.: Texture recognition for color image using 3 channels approach, pp. 898–900 (2019)
Islam, M.A., Uddin, M.A., Khan, M.N., Lee, Y.-K.: Video annotation on top of spark, pp. 762–764 (2020)
Islam, M.A., Uddin, M.A., Lee, Y.-K.: A distributed automatic video annotation platform. Appl. Sci. 10(15), 5319 (2020)
Fritz, M., Hayman, E., Caputo, B., Eklundh, J.-O.: The kth-tips database (2004)
Mallikarjuna, P., Targhi, A.T., Fritz, M., Hayman, E., Caputo, B., Eklundh, J.-O.: The kth-tips2 database. Comput. Vis. Active Percept. Lab. Stockholm, Sweden, 11, 12 (2006)
Thomas, A., Sreekumar, K.: A survey on image feature descriptors-color, shape and texture. Int. J. Comput. Sci. Inf. Technol. 5(6), 7847–7850 (2014)
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Misti, M.M.A., Mondal, S., Abir, M.A.I., Islam, M.Z. (2023). A Novel Texture Descriptor Evaluation Window Based Adjacent Distance Local Binary Pattern (EADLBP) for Image Classification. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_25
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DOI: https://doi.org/10.1007/978-3-031-34619-4_25
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