Comparing image classification methods: K-nearest-neighbor and support-vector-machines

Authors:
Jinho Kim;Byung-Soo Kim;Silvio Savarese
Affiliations:
Okemos High School, Okemos, MI;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
Venue:
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Year:
2012

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Abstract

In order for a robot or a computer to perform tasks, it must recognize what it is looking at. Given an image a computer must be able to classify what the image represents. While this is a fairly simple task for humans, it is not an easy task for computers. Computers must go through a series of steps in order to classify a single image. In this paper, we used a general Bag of Words model in order to compare two different classification methods. Both K-Nearest-Neighbor (KNN) and Support-Vector-Machine (SVM) classification are well known and widely used. We were able to observe that the SVM classifier outperformed the KNN classifier. For future work, we hope to use more categories for the objects and to use more sophisticated classifiers.