Batik image retrieval using convolutional neural network

Prasetyo, Heri; Akardihas, Berton Arie Putra

doi:10.12928/telkomnika.v17i6.12701

Cited by 26 publications

(19 citation statements)

References 16 publications

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“…Residual Network (ResNet) is an architecture of CNN which was proposed by Kaiming He [1] The architecture won the ILSVRC'15 classification in 2015 with a top error of 3.57. This residual network used the skip connection concept, as shown in Fig.…”

Section: B Proposed Methodsmentioning

confidence: 99%

“…Many studies have been carried out regarding batik starting from batik image retrieval using the CNN method [1] and Local Binary Pattern [2]. Research on the automation of batik motif classification has also been carried out using machine learning methods such as Gray Level Co-occurrence Matrix (GLCM) [3]- [7], Support Vector Machine (SVM) [8], Multi Texton Co-occurrence Descriptor (MTCD) [9], and Backpropagation [10].…”

Section: Introductionmentioning

confidence: 99%

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Batik Classification Using Convolutional Neural Network with Data Improvements

Meranggi

Yudistira

Sari

2022

JOIV : Int. J. Inform. Visualization

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Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assisted with artificial intelligence. Machine learning and deep learning have produced much good performance in image recognition. In this study, we use deep learning based on a Convolutional Neural Network (CNN) to automate the classification of batik motifs. There are two datasets used in this study. The old dataset comes from a public repository with 598 data with five types of motifs. Meanwhile, the new dataset updates the old dataset by replacing the anomalous data in the old dataset with 621 data with five types of motifs. The lereng motif is changed to pisanbali due to the difficulty of obtaining the lereng motif. Each dataset was divided into three ways: original, balance patch, and patch. We used ResNet-18 architecture, which used a pre-trained model to shorten the training time. The best test results were obtained in the new dataset with the patch way of 88.88 % ±0.88, and in the old dataset, the best accuracy was found in the patch way on the test data of 66.14 % ±3.7. The data augmentation in this study did not significantly affect the accuracy because the most significant increase in accuracy is only up to 1.22%.

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Section: B Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Batik Classification Using Convolutional Neural Network with Data Improvements

Meranggi

Yudistira

Sari

2022

JOIV : Int. J. Inform. Visualization

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“…CNN is one of the most popular deep learning methods used for recognizing and classifying images and belongs to the supervised learning category [14], [15]. CNN is a feed forward neural network inspired by biology [16], [17].…”

Section: Research Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…It consists of the input layer, convolution layer, pooling layer, and fully connected layer. In the convolution layer and pooling layer, there can be more than one and send the data to the fully connected layer [14], [19].…”

Section: Research Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Thai digit handwriting image classification with convolutional neural networks

Khunratchasana

Treenuntharath

2022

IJEECS

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This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through Cloud Computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, mini-batch size of 128, the learning rate of 0.0001, and using an Adam optimizer. This study found the model's predictive accuracy was 96.88 and the loss was 0.1075. The results showed that using CNN in image classification and recognition. It has a high level of prediction efficiency. However, the parameters in the model must be adjusted accordingly.

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“…There is another work [17] uses the Siamese CNN, multiple instances of the same model, for classify the x-ray image of the chest to classify the pneumonia disease. This paper was focused on the classification of the x-ray image in to any of the three classes.…”

Section: Litreature Reviewmentioning

confidence: 99%

An image-based gangrene disease classification

Nair

Berihu

Kumar

2020

IJECE

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Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

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Batik image retrieval using convolutional neural network

Cited by 26 publications

References 16 publications

Batik Classification Using Convolutional Neural Network with Data Improvements

Batik Classification Using Convolutional Neural Network with Data Improvements

Thai digit handwriting image classification with convolutional neural networks

An image-based gangrene disease classification

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