Making a Batik Dataset for Text to Image Synthesis Using Generative Adversarial Networks

Amalia, Aifa Nur; Huda, Arief Fatchul; Ramdania, Diena Rauda; Irfan, Mohamad

doi:10.1109/icwt47785.2019.8978233

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Their method receives two patches of batik patterns as input to synthesis a new batik image. BatikGAN [27] shows better performance compared with [25], [26]. The generated images present more details in the pattern where Amalia's approach [25] and Abdurrahman's approach [26] have more unstructured patterns if we scale the image.…”

Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 96%

“…BatikGAN [27] shows better performance compared with [25], [26]. The generated images present more details in the pattern where Amalia's approach [25] and Abdurrahman's approach [26] have more unstructured patterns if we scale the image. The three designed discriminators, such as pretrained VGG-19, global discriminator, and local discrimina tor, improve the quality of synthesis because each discriminator focuses on one task.…”

Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 96%

“…Figure 1 shows the general architecture used in [25]- [27]. Amalia et al [25] and Abdurrahman et al [26] use a standard generative adversarial network, as shown in Figure 1a. The difference is the construction of generator and discriminator.…”

Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 99%

“…Synthesize approach [25] GAN with captions [26] deep convolutional generative adversarial networks with modification [27] BatikGAN, BatikGAN with style, BatikGAN with style and local features Multi texton co-occurence descriptor acc: 100% [20] pretrained VGG16 + radom forest acc: 97.50 +-2.32% [29] Multi texton histogram and probabilistic neural network acc: 92% [30] Multi texton histogram + k-nearest neighbours acc: 82% [31] Multi structure co-occurence descriptor avg precision: 81.47% Table 6 Gultom dataset state of the art Authors Method Accuracy [32] pretrained VGG19 89+-2.8% [19] pretrained VGG16 89+-7% [15] deep neural networks 85.57%…”

Section: Authorsmentioning

confidence: 99%

See 3 more Smart Citations

Batik’s Pattern Recognition and Generation: Review and Challenges

Arsa

Susila

Dewi

et al. 2022

JIM

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Batik is one of cultural heritage acknowledged by UNESCO. Intelligence system comes as one of solution to take parts on preservation programs of this heritage. This study explores the current state of the art in application of artificial intelligence on Batik images. This research provides a systematic investigation and present the current progress and hot issues in recognition and generation area for Batik images. Furthermore, this research also presents several Batik data sets and their state of the art. As a result of the review, we are projecting several future works in the discussion.

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Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 96%

Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 96%

Section: Automatic Batik's Pattern Generation and Synthesismentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

See 2 more Smart Citations

Batik’s Pattern Recognition and Generation: Review and Challenges

Arsa

Susila

Dewi

et al. 2022

JIM

View full text Add to dashboard Cite

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“…Batik is generally created by craftsmen using specific techniques, including dyeing, printing, stamping, and dabbing. Each technique has a different difficulty level, thereby depicting imperfection in drawing Batik pattern [3], [4]. Every craftsman who draws Batik can also not necessarily draw various types of Batik.…”

Section: Introductionmentioning

confidence: 99%

Generation of Batik Patterns Using Generative Adversarial Network with Content Loss Weighting

Minarno¹,

Antoko²,

Azhar³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Every craftsman who draws Batik can also not necessarily draw various types of Batik. On the other hand, it takes a long time ranging from weeks to months, to make Batik. Image generation is regarded as an essential part of the field of computer vision. One of the popular methods includes the Generative Adversarial Network, commonly implemented to generate a new data set from an existing one. One model of the Generative Adversarial Network is BatikGAN SL generating batik images by inserting the two Batik patterns to produce a new Batik image. Currently, the generated Batik image does not maintain the input of the Batik pattern. Therefore, this study proposes a GAN model of BatikGAN SL, with the addition of a content loss function using hyperparameters to weight the content loss function. The content loss function is added from the Neural Transfer Style method. Previously, the style loss function in this method has been implemented in BatikGAN SL, and the dataset consists of Batik patches (326 images) and real Batik (163 images). This paper compares the BatikGAN SL model from previous studies with the BatikGAN SL model by implementing hyperparameters on the content loss function. The evaluation is conducted with FID, containing FID Local and FID Global. The results obtained in this study include a collection of Batik images, test evaluation value of 42 on FID Global and 16 on FID Local. These results are obtained by implementing the content loss function with a weight value of 1.

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