Image classification is a challenging task in computer vision. For example fully understanding real-world images may involve both scene and object recognition. Many approaches have been proposed to extract meaningful descriptors from images and classifying them in a supervised learning framework. In this chapter, we revisit the classic k-nearest neighbors (k-NN) classification rule, which has shown to be very effective when dealing with local image descriptors. However, k-NN still features some major drawbacks, mainly due to the uniform voting among the nearest prototypes in the feature space. In this chapter, we propose a generalization of the classic k-NN rule in a supervised learning (boosting) framework. Namely, we redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. In order to induce this classifier, we propose a novel learning algorithm, MLNN (Multiclass Leveraged Nearest Neighbors), which gives a simple procedure for performing prototype selection very efficiently. We tested our method first on object classification using 12 categories of objects, then on scene recognition as well, using 15 real-world categories. Experiments show significant improvement over classic k-NN in terms of classification performances. © 2013 Springer Science+Business Media.
CITATION STYLE
Piro, P., Barlaud, M., Nock, R., & Nielsen, F. (2013). K-NN boosting prototype learning for object classification. In Lecture Notes in Electrical Engineering (Vol. 158 LNEE, pp. 37–53). https://doi.org/10.1007/978-1-4614-3831-1_3
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