Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Generally, CBIR includes two main components, namely feature extraction and similarity measurement. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. In recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the combination of texture and shape features in batik image. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features.
The value of shape features is extracted using a binary morphological operation which includes compactness, eccentricity, rectangularity and solidity. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) which include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different moment (IDM). This study aims to combine the features of texture and the feature of shapes' ornament in batik to classify images using artificial neural networks. Bat ik has many motifs which are classified in various classes of batik. Batik is a textile with motifs of Indonesian culture which has been recognized by UNESCO as world cultural heritage.
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