SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can ...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Alexander C. Berg, Hao Zhang 0003, Jitendra Malik, Michael Maire
• Keywords: Computer Vision, CVPR 2006, Multiclass Data Sets, Multiclass Setting, Nearest Neighbor Classifiers, Support Vector Machines, Visual Category Recognition