Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

Nanik Suciati; Darlis Herumurti; Arya Yudhi Wijaya

doi:10.1117/12.2266933

8 February 2017 Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

Nanik Suciati, Darlis Herumurti, Arya Yudhi Wijaya

Author Affiliations +

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022502 (2017) https://doi.org/10.1117/12.2266933
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Batik is one of Indonesian’s traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.

Citation Download Citation

Nanik Suciati, Darlis Herumurti, and Arya Yudhi Wijaya "Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022502 (8 February 2017); https://doi.org/10.1117/12.2266933

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