Content-Based Image Retrieval for Songket Motifs using Graph Matching
DOI:
10.33395/sinkron.v7i2.11411Abstract
Indonesia is a country that has abundant cultural wealth. One of the characteristics of Indonesian culture is Songket. Songket is a typical Malay woven cloth that has many variants of motifs, each of which represents a different meaning and philosophy. Songket is often found in Sumatra Island with different motifs in each region. With so many types of songket motifs, not everyone can recognize and distinguish between one songket motif and another, even Indonesian citizens themselves. With the help of computers, it is easier to find information about a songket motif or to find a similar songket motif. The field that can play a role in solving this problem is Content-Based Image Retrieval (CBIR). This study aims to carry out a content retrieval process on the songket core motif using graph matching-based processing. In this study, the method used is felzenzswalb segmentation, and graph matching through the VF2 isomorphism algorithm and graph edit distance. The number of songket core motif images used as data is 180 data in the form of color images measuring 64 x 64 pixels. Based on the results of the study, it was found that the optimal graph matching algorithm and parameters in this study were the VF2 algorithm for artificial images with an f-1 score of 91.05%, and Graph Edit Distance with GED≤8 parameters for songket motif images with an f1-score. by 53.36%.
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Baso, B., & Suciati, N. (2020). East Nusa Tenggara Weaving Image Retrieval Using Robust Feature Extraction Against Changes in Scale, Rotation and Lighting. Journal of Information Technology and Computer Science (JTIIK) , 7 (2), 349–358. https://doi.org/10.25126/jtiik.20207202
Brun, L., Foggia, P., & Vento, M. (2020). Trends in graph-based representations for Pattern Recognition. Pattern Recognition Letters , 134 , 3–9. https://doi.org/https://doi.org/10.1016/j.patrec.2018.03.016
Felzenszwalb, PF, & Huttenlocher, DP (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision , 59 (2), 167–181. https://doi.org/10.1023/B:VISI.000022288.19776.77
Hasan, MA, & Liliana, DY (2020). Recognition of Palembang Songket Motifs Using Canny Edge Detection, PCA and KNN. Multinetics , 6 (1), 1–7. https://doi.org/10.32722/multinetics.v6i1.2700
Holzinger, A., & Jurisica, I. (2014). Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. In A.
Holzinger & I. Jurisica (Eds.), Interactive Knowledge Discovery and Data Mining in Biomedical Informatics (pp. 1–18). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43968-5_1
Imran, B., & Efendi, MM (2020). the Implementation of Extraction Feature Using GLCM and Back-Propagation Artificial Neural Network to Classify Lombok Songket Woven Cloth. Journal of Techno Nusa Mandiri , 17 (2), 131–136. https://doi.org/10.33480/techno.v17i2.1680
Malmberg, F., & Ciesielski, KC (2020). Two Polynomial Time Graph Labeling Algorithms Optimizing Max-Norm-Based Objective Functions. Journal of Mathematical Imaging and Vision , 62 (5), 737–750. https://doi.org/10.1007/s10851-020-00963-8
Martey, EM, Lei, H., Li, X., & Appiah, O. (2021). Effective Image Representation using Double Color Histogram for Content-Based Image Retrieval. Informatica , 45 (7), 97–105. https://doi.org/10.31449/inf.v45i7.3715
Oksaputri, R., Ernawati, E., & Andreswari, D. (2018). Implementation of Content Based Image Retrieval (CBIR) on Incomplete Besurek Batik Images Using Speeded Up Robust Features (SURF) and Fast Library Approximated Nearest Neighbor (FLANN) methods. Pseudocode , 5 (2), 18–28. https://doi.org/10.33369/pseudocode.5.2.18-28
Prasetyo, H., Wiranto, Winarno, Salamah, U., & Harjito, B. (2018). Batik Image Retrieval using ODBTC Feature and Particle Swarm Optimization. Journal of Telecommunication, Electronic and Computer Engineering , 10 (2–4), 71–74.
Sanfeliu, A., & Fu, K.-S. (1983). A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics , SMC - 13 (3), 353–362. https://doi.org/10.1109/TSMC.1983.6313167
Sharma, H., Alekseychuk, A., Leskovsky, P., Hellwich, O., Anand, RS, Zerbe, N., & Hufnagl, P. (2012). Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics. Diagnostic Pathology , 7 (1), 1–20. https://doi.org/10.1186/1746-1596-7-134
Yohannes, Y., Devella, S., & Pandrean, AH (2019). Application of Speeded-Up Robust Feature in Random Forest for Classification of Palembang Songket Motifs. Journal of Informatics and Information Systems Engineering , 5 (3), 360–369. https://doi.org/10.28932/jutisi.v5i3.1978
Yuhandri, Madenda, S., Wibowo, EP, & Karmilasari. (2017). Pattern Recognition and Classification using Backpropagation Neural Network Algorithm for Songket Motifs Mmage Retrieval. International Journal on Advanced Science, Engineering and Information Technology , 7 (6), 2343–2349. https://doi.org/10.18517/ijaseit.7.6.2200
Yusof, N., Ismail, A., & Majid, NAA (2019). Visualising Image Data through Image Retrieval Concept Using a Hybrid Technique: Motif's Songket. International Journal of Advanced Computer Science and Applications , 10 (12), 359–369. https://doi.org/10.14569/ijacsa.2019.0101248
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Copyright (c) 2022 Yullyana Yullyana, Deci Irmayani, Mila Nirmala Sari Hasibuan
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