Motif-Sikka: A Curated Image Dataset of Traditional Sikka Ikat Weaving Motifs for Textile Pattern Analysis
Description
This dataset was created under the hypothesis that image-based computational methods can effectively be used to classify and recognize traditional textile motifs, particularly Sikka ikat weaving patterns. Variations in lighting, device resolution, and motif structure are significant challenges in textile image analysis. By capturing these variations in structured dataset, we hypothesize that it will support the development of more robust and generalizable computer vision models for motif recognition and cultural heritage preservation. This dataset is a subset of the Sikka-MoDiVers collection, consisting of 432 images from 24 verified classes of traditional Sikka ikat weaving motifs. Each class is represented by images captured under six natural lighting conditions: Indoor morning, Indoor noon, Indoor night, Outdoor morning, Outdoor noon, Outdoor night The images have been resized to 512×512 pixels to standardize them for machine learning tasks such as image enhancement, segmentation, feature extraction, and motif classification. The naming convention of image files reflects both the motif class and the lighting condition. Each motif class is stored in its own folder, with clear labeling. The dataset is aligned with the motif classification from the Geographical Indication Certificate ID G 0000000564, issued by the Directorate General of Intellectual Property, Republic of Indonesia. Included in this release: 1. A metadata CSV file (classes.csv) listing motif name, number of images, and lighting conditions. 2. A README file (README_v3.md) that describes the directory structure, naming format, and usage instructions.tudies involving traditional Indonesian textiles. Potential Uses: 1. Deep learning model training for traditional textile recognition. 2. Cultural heritage digital archiving and motif classification. 3. Evaluation of enhancement techniques under varying lighting conditions. How to Use: Extract the dataset and refer to the metadata file for understanding the structure. Each folder contains images from a single class, suitable for direct use in supervised learning models.
Files
Steps to reproduce
The dataset was constructed through a structured image acquisition and verification workflow. Traditional Sikka ikat weaving fabrics were selected from weaver groups and textile sellers across various locations in the Sikka region, East Nusa Tenggara, Indonesia. The motifs were identified and labelled based on local knowledge and expert consultation. Image were captured using two types of device to reflect natural variability in resolution and lighting: - DSLR camera : Niko D5100 - Smartphones: POCO X3, Vivo Y35, Oppo Reno 6, and other mid-to-high range devices Each motif was photographed under three different natural lighting conditions: - Morning - Noon - Night Capture locations varied between indoor and outdoor settings such as weaving workshops, homes, and cultural events. No artificial lighting, editing, or filtering was applied to the images. Motif names and class labels were assigned manually by the research team and verified by experts in traditional Sikka textiles. Classes were categorized based on visual and cultural characteristics of the motifs. Metadata was created for each image, including: - Filename - Motif Class - Image resolution (width x height) - Lighting condition - Device used - Locations The dataset is organized into folders by motif class, with filenames indicating capture conditions. Three metadata file accompany the dataset: - metadata_motif.csv: technical and contextual info per image - class_description_motif.csv: cultural meaning of each motif - motif_cass_status.csv: verification status and availability Researchers may reproduce this dataset by applying similar acquisition workflows using various capture devices, natural lighting setups, and cultural expert validation on other regional textile collections.
Institutions
- Universitas Gadjah Mada
- Universitas Nusa Nipa