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liboyin / img-classify

Household Object Recognition Challenge
GNU General Public License v2.0
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A new voting scheme #2

Closed liboyin closed 9 years ago

liboyin commented 9 years ago

Currently, each SIFT descriptor from a training image is assumed to be representative for that class. Each SIFT descriptor from a test image is classified with kNN, and votes for the classification of the test image. Here I propose another voting scheme. We consider a class as the set of all SIFT descriptors of all training images of that class. For a test image, we compare each of its SIFT descriptor to each SIFT descriptor of every class. We assign each class a score that is the smallest distance between any of its SIFT descriptors to the current SIFT descriptor from the test image. The test image is classified to the class with the smallest sum of such scores.

liboyin commented 9 years ago

Libo is doing it.

liboyin commented 9 years ago

I've implemented and tested it. The result is surprisingly bad with <5% accuracy. I'm going to try a few different metric functions, both on this algorithm and the original kNN.

liboyin commented 9 years ago

New voting scheme with Euclidean distance achieved 91% accuracy. Woohoo!