Evaluating the Robustness of Feature Correspondence using Different Feature Extractors

The importance of choosing a suitable feature detector and descriptor to find the optimal correspondence between two sets of image features has been highlighted. In this direction, this paper presents an evaluation of some well known feature detectors and descriptors; including HARRIS-FREAK, HESSIAN-SURF, MSER-SURF, and FAST-FREAK; in the search for an optimal detector and descriptor pair that best serves the matching procedure between two images. The adopted matching algorithm pays attention not only to the similarity between features but also to the spatial layout in the neighborhood of every matched feature. The experiments conducted on 50 images; representing 10 objects from COIL-100 data-set with extra synthetic deformations; reveal that HARRIS-FREAK's extractor results in better feature correspondence.