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A Vector Quantization Based k-NN Approach for Large-Scale Image Classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA)
PublisherIEEE
ISBN (Electronic)978-1-4673-8910-5
ISBN (Print)978-1-4673-8911-2
DOIs
Publication statusPublished - Dec 2016
Publication typeA4 Article in a conference publication
EventInternational Conference on Image Processing Theory, Tools and Applications -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2154-512X

Conference

ConferenceInternational Conference on Image Processing Theory, Tools and Applications
Period1/01/00 → …

Abstract

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.

Publication forum classification

Field of science, Statistics Finland