A vector quantization based k NN approach for large scale image classification
The k nearest neighbour classifiers (k NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and the number of dimensions of image descriptors, popularity of k NNs has decreased due to their significant storage requirements and computational costs. In this paper we propose a vector quantization (VQ) based k NN classifier, which has improved efficiency for both storage requirements and computational costs. We test the proposed method on publicly available large scale image datasets and show that the proposed method performs comparable to traditional k NN with significantly better complexity and storage requirements.
2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)