Pengenalan Motif Batik Menggunakan Rotated Wavelet Filterdan Neural Network

Arisandi, Bernardinus; Suciati, Nanik; Wijaya, Arya Yudhi

doi:10.12962/j24068535.v9i2.a34

Cited by 14 publications

(15 citation statements)

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“…Batik pattern recognition is performed using Rotate Wavelet Filter and combined with artificial neural networks. Rotate Wavelet Filter is used for batik feature extraction that will be recognized and the neural network is used to classify image based on the pattern [16]. Wavelet is used to define multi-resolution spaces and result in energy characteristics and standard deviation with image size of 128 x 128 pixels.…”

Section: The Development Of Batik Feature Extraction Methodsmentioning

confidence: 99%

Feature extraction methods for batik pattern recognition: A review

Kasim¹,

Wardoyo²,

Harjoko³

2016

AIP Conference Proceedings

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Section: The Development Of Batik Feature Extraction Methodsmentioning

confidence: 99%

Feature extraction methods for batik pattern recognition: A review

Kasim¹,

Wardoyo²,

Harjoko³

2016

AIP Conference Proceedings

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“…Penelitian pengenalan motif sebelumnya dilakukan pada motif batik. Beberapa algoritma yang dapat diterapkan dalam pengenalan motif batik dikemukakan oleh B. Arisandi menggunakan Rotated Wavelet Filter dan Neural Network dengan akurasi 100% pada data testing sama dengan data training serta akurasi 78,26% pada data training berbeda [5]. Berikutnya M. Suryawan menulis, identifikasi motif batik dengan algoritma C4.5 berdasarkan orientasi objek [6].…”

Section: Pendahuluan A) Latar Belakangunclassified

Pengenalan Motif Karawo Menggunakan Ekstraksi Fitur SIFT Dan Approximate Nearest Neighbor

Syahrial¹

2019

Preprint

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An art culture from Gorontalo became iconic handcraft is kerawang or karawo. The word “karawo” came from root word of “mokarawo” which means slicing or making holes. It’s created with precision, carefulness, and patience in work using handmade masterpiece. Pattern of karawo itself held four kinds which is flora, fauna, geometric, and nature. From those kinds born vary pattern which come difficult to identify both its names and its kind. Karawo patterns can be form as a single pattern or a pattern that it parts came from several or many pattern combined. Those patterns had its own characteristic from shape and scale perspective. Identifying single pattern on combined pattern are particularly a problem because it’s combined involve scaling and rotation. This research is recognizing single pattern on combined pattern using feature extraction SIFT algorithm which is capable extract feature that invariant from scale and rotation. Feature matching using approximate nearest neighbor (aNN) for similarity of features labor best bin first strategy on kd-tree data structure. Those methods can be a reference to recognize single pattern on combined pattern using from range 5 to 20 match features as a threshold. Testing result indicated recognition accuracy is good which range form 76.36% to 85.45% on recognize the kind of karawo pattern and 76.36% on its name.

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“…Some other feature extraction methods that have been implemented in CBIR are Rotated Wavelet Filter [2], MultiTexton Co-occurrence Descriptor [3], Color String Representation [4] and combination of Color Co-occurrence Matrix, Pixels Patterns, and Color Histogram [5].In addition to the using of low level features of an image, some efforts in exploring high-level features, namely conceptual and semantic features, also had been reported [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Fractal-based texture and HSV color features for fabric image retrieval

Suciati

Herumurti

Wijaya

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Wide range of products such as clothing, bed linen, curtains, and shoes, use fabrics as main raw material. Fabrics have various types of materials, colors and patterns. Harmony in combiningthe various types of fabrics will affect the beauty of the resulted product. A system that can be used to retrieve some fabrics similar to a fabrics sample automatically will facilitate the combining process in creating a product. In this study, a fabrics image retrieval system using combination of fractal-based texture feature and HSV color feature is developed. The Canberra Distance is used to measure similarity between features vectors. The experiment which is done using two kinds of fabrics image datasets, i.e. "batik" and "common", gives average recall 94% and 92%, respectively.IndexTerms-Image retrieval, fractal-based texture, HSV color.

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Pengenalan Motif Batik Menggunakan Rotated Wavelet Filterdan Neural Network

Abstract: ABSTRAK

Cited by 14 publications

References 1 publication

Feature extraction methods for batik pattern recognition: A review

Feature extraction methods for batik pattern recognition: A review

Pengenalan Motif Karawo Menggunakan Ekstraksi Fitur SIFT Dan Approximate Nearest Neighbor

Fractal-based texture and HSV color features for fabric image retrieval

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