Performance Evaluation of SIFT Descriptor against Common Image Deformations on Iban Plaited Mat Motifs
This work addresses the problem of preserving indigenous craft heritage by developing a mobile app for motif recognition, but it is incremental as it applies an existing method to a new dataset.
The study evaluated the SIFT descriptor's performance on a new dataset of Iban plaited mat motifs against five common image deformations, finding it performed well for illumination, viewpoint, JPEG compression, and zoom/rotation changes but poorly for image blur with only 1.61% retained pairwise matching.
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.