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      Batik Nitik 960 Dataset for Classification, Retrieval, and Generator

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          Abstract

          Batik is one of the traditional heritages of Indonesia, with each motif of batik having a profound cultural and philosophical significance. This article introduces Batik Nitik 960 dataset from Yogyakarta, Indonesia. The dataset was extracted from a piece of fabric with 60 Nitik patterns. The dataset was supplied by the Paguyuban Pecinta Batik Indonesia (PPBI) Sekar Jagad Yogyakarta collection of Winotosasto Batik and the data were extracted from the APIPS Gallery. Each of the 60 categories in the collection contains 16 photographs, for a total of 960 images. The photographs were acquired with a Sony Alpha a6400, illuminated with a Godox SK II 400, and the data were compressed using the jpg file format. Each category contains four motifs rotated by 90, 180, and 270 degrees. Thus, the total number of images per motif is 16. Each class has a specific philosophical significance associated with the motif’s origins. This dataset aims to enable the training and evaluation of machine learning models for classification, retrieval, or generation of a new batik pattern using a generative adversarial network. To our knowledge, this study is the first to present a Batik Nitik dataset equipped with philosophical significance that is freely accessible.

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          Most cited references46

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          Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

          As multimedia technology is developing and growing these days, the use of an enormous number of images and its datasets is likewise expanding at a quick rate. Such datasets can be utilized for the purpose of image retrieval. This research focuses on extraction of similar images established on its different features for the image retrieval purpose from huge dataset of images. In this paper initially, the query image is searched within the available dataset and, then, the color difference histogram (CDH) descriptor is employed to retrieve the images from database. The basic characteristic of CDH is that it counts the color difference stuck among two distinct labels in the L ∗ a ∗ b ∗ color space. This method is experimented on random images used for various medical purposes. Various unlike features of an image are extracted via different distance methods. The precision rate, recall rate, and F-measure are all used to evaluate the system's performance. Comparative analysis in terms of F-measure is also made to check for the best distance method used for retrieval of images.
            • Record: found
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            Batik Classification Using Deep Convolutional Network Transfer Learning

              • Record: found
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              • Conference Proceedings: not found

              Feature Selection and Reduction for Batik Image Retrieval

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Data
                Data
                MDPI AG
                2306-5729
                April 2023
                March 24 2023
                : 8
                : 4
                : 63
                Article
                10.3390/data8040063
                a70e4eaf-0a8c-4cce-85ad-5eff9ede0300
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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