KLASIFIKASI CITRA MENGGUNAKAN MULTI TEXTON HISTOGRAM DAN PROBABILISTIC NEURAL NETWORK

Hardianto Wibowo, Agus Eko Minarno

Abstract


Pengelompokkan citra berdasarkan kategori yang sesuai sangat dibutuhkan dalam basis data citra. Beberapa bidang ilmu yang membutuhkan basis data antara lain temu kembali citra, pengenalan objek pada citra, image annotation, dan relavance feedback. Oleh karena itu penelitian ini dikembangkan tentang klasifikasi citra menggunakan multi texton histogram dan probabilistic neural network. Berdasarkan hasil uji coba, akurasi untuk data Batik mencapai nilai 92% dan data Brodatzmencapai 98%. Multi Texton Histogram dan Probabilistic Neural Network mampu mengklasifikasi citra Brodatz dan Batik dengan efektif.

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References


M. Cheong and K. S. Loke, “An approach to texture-based image recognition by deconstructing multispectral co-occurrence matrices using tchebichef orthogonal polynomials,” in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE. 2008; pp. 1–4.

K. S. Loke and M. Cheong, “Efficient textile recognition via decomposition of co-occurrence matrices.” in 2009 IEEE International Conference on signal and Image Processing Applications. 2009; pp.257-261.

I. Nurhaida, R. Manurung, and A. M. Arymurthy, “Performance comparison analysis features extraction methods for batik recognition,” in Advanced Computer Science and Information

Systems (ICACSIS), 2012 International Conference on. IEEE, 2012; pp. 207–212.

C. W. D. de Almeida, R.M. C. R. de Souza, A. L. B. Candeias, “Texture classification based on co-occurrence matrix and self-organizing map”, IEEE International conference on Systems Man& Cybernetics, University of pernambuco, Recife. 2010; pp. 2487 - 2491.

Huang, Jing, Shanmugasundaram Ravi Kumar, Mandar Mitra, and Wei-Jing Zhu. "Image indexing using color correlograms." U.S. Patent 6,246,790, issued June 12, 2001.

Guang-Hai Liu, Lei Zhang, Ying-Kun Hou, Zuo Yong Li, Jing Yu Yang. 2010. Image Retrieval based on multi-texton histogram. Journal of Pattern Recognition 43 (2010). 2380-2389. Science

Direct.

Haralick, Robert M. "Statistical and structural approaches to texture." Proceedings of the IEEE 67, no. 5 (1979): 786-804.

Agus Eko Minarno, Yuda Munarko, Fitri Bimantoro, Arie Kurniawardhani, Nanik Suciati, “Batik Image Retrieval Based on Enhanced Micro-Structure Descriptor”, 2nd Asia Pacific Conference on Computer Aided System Engineering (APCASE) 2014, Bali, 10 – 12 February 2014

Agus Eko Minarno, Yuda Munarko, Fitri Bimantoro, Arie Kurniawardhani, Nanik Suciati, “Texture Feature Extraction using Co-Occurence Matrix of Sub-Band Image for Batik Image Classification”, ICoICT 2014, Bandung, 28 – 29 May 2014

Agus Eko Minarno, Nanik Suciati, “Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurence Matrix”, TELKOMNIKA Journal, September 2014, Vol. 12, No. 3, ISSN: 1693-6930. pp.125-132.

Agus Eko Minarno, Nanik Suciati, “Image Retrieval using Multi Texton Co-Occurence Descriptor”, Journal of Theoretical and Applied Information Technology”, 2014, Vol 67 No 3, E-ISSN 1817-3195 / ISSN 1992-8645,p103-110.

Specht, D.F., Probabilistic neural networks. Neural Netw. 1990; 3, 109–118.




DOI: https://doi.org/10.22219/sentra.v0i1.2053

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Fakultas Teknik

Universitas Muhammadiyah Malang Kampus III

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