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

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA)
KustantajaIEEE
ISBN (elektroninen)978-1-4673-8910-5
ISBN (painettu)978-1-4673-8911-2
DOI - pysyväislinkit
TilaJulkaistu - joulukuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Image Processing Theory, Tools and Applications -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2154-512X

Conference

ConferenceInternational Conference on Image Processing Theory, Tools and Applications
Ajanjakso1/01/00 → …

Tiivistelmä

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.

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