In defense of Nearest-Neighbor based image classification. State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties.
We argue that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: (i) Quantization ...
We propose a trivial NN-based classifier – NBNN, (Naive-Bayes Nearest-Neighbor), which employs NN- distances in the space of the local image descriptors (and ...
It is argued that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: ...
We propose a trivial NN-based classifier – NBNN, (Naive-Bayes Nearest-Neighbor), which employs NN- distances in the space of the local image descriptors (and ...
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We propose a trivial NN-based classifier - NBNN, (Naive-Bayes nearest-neighbor), which employs NN- distances in the space of the local image descriptors (and ...
We propose a trivial NN-based classifier - NBNN, (Naive-Bayes Nearest-Neighbor), which employs NN-distances in the space of the local image descriptors (and not ...
In Defense of Nearest-Neighbor Based Image Classification
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Publications · In Defense of Nearest-Neighbor Based Image Classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Anchorage.
The most common non-parametric method is Nearest- Neighbor-Image Although this is the most popular among the NN-based image classifiers, it provides inferior ...
We present Local Naive Bayes Nearest Neighbor, an im- provement to the NBNN image classification algorithm that increases classification accuracy and improves ...