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Comparing Visual Features for Morphing Based Recognition

dc.date.accessioned2005-12-22T02:29:44Z
dc.date.accessioned2018-11-24T10:24:30Z
dc.date.available2005-12-22T02:29:44Z
dc.date.available2018-11-24T10:24:30Z
dc.date.issued2005-05-25
dc.identifier.urihttp://hdl.handle.net/1721.1/30547
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/30547
dc.description.abstractThis thesis presents a method of object classification using the idea of deformable shape matching. Three types of visual features, geometric blur, C1 and SIFT, are used to generate feature descriptors. These feature descriptors are then used to find point correspondences between pairs of images. Various morphable models are created by small subsets of these correspondences using thin-plate spline. Given these morphs, a simple algorithm, least median of squares (LMEDS), is used to find the best morph. A scoring metric, using both LMEDS and distance transform, is used to classify test images based on a nearest neighbor algorithm. We perform the experiments on the Caltech 101 dataset [5]. To ease computation, for each test image, a shortlist is created containing 10 of the most likely candidates. We were unable to duplicate the performance of [1] in the shortlist stage because we did not use hand-segmentation to extract objects for our training images. However, our gain from the shortlist to correspondence stage is comparable to theirs. In our experiments, we improved from 21% to 28% (gain of 33%), while [1] improved from 41% to 48% (gain of 17%). We find that using a non-shape based approach, C2 [14], the overall classification rate of 33.61% is higher than all of the shaped based methods tested in our experiments.
dc.format.extent42 p.
dc.format.extent39773758 bytes
dc.format.extent1459526 bytes
dc.language.isoen_US
dc.subjectAI
dc.subjectobject recognition
dc.subjectshape-based
dc.titleComparing Visual Features for Morphing Based Recognition


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