2018/10/02 by Silvia Joseph, Joseph, Silvia, Hipiny, Irwandi +3
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1810.01562
14th International Borneo Research Council Conference, 6 to 8 August 2018, UNIMAS, Sarawak
openalex publication_date 2018/10/02 · arxiv created 2018/10/03 · arxiv updated 2018/10/04 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Borneo indigenous communities are blessed with rich craft heritage. One such examples is the Iban's plaited mat craft. There have been many efforts by UNESCO and the Sarawak Government to preserve and promote the craft. One such method is by developing a mobile app capable of recognising the different mat motifs. As a first step towards this aim, we presents a novel image dataset consisting of seven mat motif classes. Each class possesses a unique variation of chevrons, diagonal shapes, symmetrical, repetitive, geometric and non geometric patterns. In this study, the performance of the Scale invariant feature transform (SIFT) descriptor is evaluated against five common image deformations, i.e., zoom and rotation, viewpoint, image blur, JPEG compression and illumination. Using our dataset, SIFT performed favourably with test sequences belonging to Illumination changes, Viewpoint changes, JPEG compression and Zoom and Rotation. However, it did not performed well with Image blur test sequences with an average of 1.61 percents retained pairwise matching after blurring with a Gaussian kernel of 8.0 radius.