  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.
