Performance Evaluation of SIFT Descriptor against Common Image Deformations on Iban Plaited Mat Motifs

TL;DR

This study evaluates the robustness of the SIFT image descriptor against common deformations on a new dataset of Iban mat motifs, highlighting its strengths and weaknesses for cultural heritage recognition.

Contribution

It introduces a novel dataset of Iban mat motifs and systematically assesses SIFT's performance against various image deformations.

Findings

SIFT performs well under illumination, viewpoint, JPEG compression, and zoom/rotation.

SIFT performs poorly on blurred images, retaining only 1.61% matches after heavy blurring.

The dataset supports future research in motif recognition and cultural heritage preservation.

Abstract

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.