Semantic image search system of madurese cultural batik with impression, color and shape features

Umam, Khotibul; Barakbah, Ali Ridho; Basuki, Achmad

doi:10.1109/kcic.2016.7883621

Cited by 3 publications

(6 citation statements)

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“…Komputasi budaya merupakan penelitian yang bertujuan untuk mengatasi berbagai masalah budaya di masyarakat dengan teknologi informasi dan komunikasi. [1] dalam penelitiannya komputasi budaya diaplikasikan ke dalam batik Madura untuk memperoleh impressi dari motif batik Madura melalui aplikasi mobile. Impressi diperoleh dari hasil ektraksi fitur warna, bentuk, dan area.…”

Section: Pendahuluanunclassified

Komputasi Budaya Untuk Pencarian Gambar Semantik Pada Lukisan Budaya Indonesia Dengan Deteksi Dan Informasi Aliran Lukisan

Winedhar

Barakbah

Basuki

et al. 2021

J-TIT

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Lukisan merupakan salah satu gambaran kompleks yang mencerminkan pengamatan dan perasaan seniman terhadap lingkungan. Kondisi ini memperluas kebutuhan akan sistem pendeteksi citra budaya karena masyarakat awam yang kurang memiliki pengalaman artistik akan sulit mendapatkan kesan lukisannya. Oleh karena itu, peneliti menekankan penerapan lukisan budaya Indonesia ke dalam aplikasi mobile. Sistem yang diusulkan telah diimplementasikan pada 239 lukisan budaya Indonesia yang terdiri dari lima kategori gaya lukisan. Kategorinya adalah abstraksionisme, naturalisme, ekspresionisme, realisme, dan romantisme. Sistem mengekstrak 3 fitur, yaitu fitur warna, bentuk, dan tekstur. Ekstraksi ciri warna menggunakan Histogram 3D Color Vector Quantization. Ekstraksi fitur bentuk menggunakan Connected Component Labeling Algorithm (CCL) dengan menghitung nilai area, diameter setara, luas, convex hull, soliditas, eksentrisitas, dan perimeter masing-masing objek. Ekstraksi fitur tekstur menggunakan Gabor Transformation dengan 40 kernel. Sedangkan untuk ekstraksi impresi dilakukan survey terhadap beberapa orang tentang impresi lukisan budaya Indonesia. Survei ini dilakukan terhadap responden yang memahami seni lukis seperti pelukis, pemerhati lukisan, dan orang-orang yang berkecimpung di dunia seni rupa. Untuk menunjukkan gaya lukisan peneliti menggunakan proses klasifikasi menggunakan K-Nearest Neighbor. Hasil eksperimen menunjukan fitur warna sebagai fitur terbaik dalam impression query

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Section: Pendahuluanunclassified

Komputasi Budaya Untuk Pencarian Gambar Semantik Pada Lukisan Budaya Indonesia Dengan Deteksi Dan Informasi Aliran Lukisan

Winedhar

Barakbah

Basuki

et al. 2021

J-TIT

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“…Indonesia has the traditional fabric recognized as one of the country's cultural heritage in the form of batik and sarong fabric. Batik has various patterns and motifs which derive from several areas, such as Java [10,11], central Sulawesi [9], and Madura [12]. Samarinda is one of the cities in East Kalimantan-Indonesia's largest provincehas a distinctive sarong that residents typically wear for formal or religious occasions.…”

Section: Introductionmentioning

confidence: 99%

“…Three types of features were extracted throughout the feature extraction process: color [12,16,17], texture [10,11], and shape [10,12,16,17]. Color moments and histograms were used to generate the color features [12]. Meanwhile, GLCM [9,11], LBP [8], and wavelet transform [13] were extracted as texture features.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, GLCM [9,11], LBP [8], and wavelet transform [13] were extracted as texture features. Furthermore, shape features were generated using moment Invariant [12] and area- based [7,10]. The task of fabric classification was accomplished using machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…The task of fabric classification was accomplished using machine learning. Several classifiers have been implemented, including K-Nearest Neighbor (KNN) [8,12], Decision Tree [13], Support Vector Machine (SVM) [9,14], and ANN [7,8,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

2022

IJIES

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Sarong is a traditional cloth typically worn during formal or religious events which conventionally woven using the traditional loom. Samarinda is one of Indonesia's regions with a sarong with a distinctive pattern. Nevertheless, the majority of indigenous people cannot distinguish the various motifs of Samarinda sarongs from those of other regions in Indonesia (non-Samarinda). Therefore, it is necessary to classify the motif of sarongs. This work proposed a pattern recognition method based on computer vision for sarongs motif classification. This method required adequate features to achieve the optimal results. Accordingly, the appropriate color and texture features were investigated to obtain the most discriminative ones. This work generated features using color moments, Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP). The most discriminatory features were selected using the Correlation Based Feature Selection (CFS) and then fed into Artificial Neural Network (ANN). A total of 1000 images were used to evaluate the method and achieved the highest performance with an accuracy value of 100%.

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Review on integration of ontology and deep learning in cultural heritage image retrieval

Budiman,

Sugiarto,

Hendriyanto

2024

IJEECS

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Image retrieval methods are currently developing towards big data processing. The literature review is focused on image big data extraction with cultural heritage domain as training and testing datasets. The development of image retrieval process starts from content-based using machine algorithms, deep learning to ontology-based. Image recognition research with cultural heritage domain is conducted because of the importance of preserving and appreciating cultural heritage, in this case, cultural heritage images such as Indonesian Batik are discussed. Batik motif images are Indonesian cultural heritage that has thousands of motifs that are grouped into many classes with a non-linear hyperplane. The problem is focused on processing big data that has many classes. Currently research is evolving into knowledge-based image retrieval using ontologies due to semantic gap constraints. The results of this literature study can be the basis for developing research on the application of appropriate deep learning algorithms so as to utilize the hierarchy of classes and subclasses of image ontologies with cultural heritage domains.

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Semantic image search system of madurese cultural batik with impression, color and shape features

Cited by 3 publications

References 0 publications

Komputasi Budaya Untuk Pencarian Gambar Semantik Pada Lukisan Budaya Indonesia Dengan Deteksi Dan Informasi Aliran Lukisan

Komputasi Budaya Untuk Pencarian Gambar Semantik Pada Lukisan Budaya Indonesia Dengan Deteksi Dan Informasi Aliran Lukisan

Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

Review on integration of ontology and deep learning in cultural heritage image retrieval

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