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Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization. In this paper, we introduce a kernelized version of NBNN. This way, we can learn the classifier...
Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes...
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