Report: Reducing the error rate of a Cat classifier
نویسنده
چکیده
The following report discusses my work at the IDIAP from 06.2007 to 08.2007. This work had for objective to build a classifier for cat images, and focused on difficult images. Two approaches were implemented; one was using the Chamfer distance metric for matching, and the other learned models of our training images and used the Kullback-Leibler divergence as a metric of image comparison. Noise in the training and testing image proved to be an overwhelming problem, tainting our results. Control experiments, however indicated sound procedures.
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