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

Tutkimustuotosvertaisarvioitu

Standard

A Vector Quantization Based k-NN Approach for Large-Scale Image Classification. / Ozan, Ezgi Can; Riabchenko, Ekaterina; Kiranyaz, Serkan; Gabbouj, Moncef.

2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA). IEEE, 2016.

Tutkimustuotosvertaisarvioitu

Harvard

Ozan, EC, Riabchenko, E, Kiranyaz, S & Gabbouj, M 2016, A Vector Quantization Based k-NN Approach for Large-Scale Image Classification. julkaisussa 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA). IEEE, International Conference on Image Processing Theory, Tools and Applications, 1/01/00. https://doi.org/10.1109/IPTA.2016.7821010

APA

Ozan, E. C., Riabchenko, E., Kiranyaz, S., & Gabbouj, M. (2016). A Vector Quantization Based k-NN Approach for Large-Scale Image Classification. teoksessa 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA) IEEE. https://doi.org/10.1109/IPTA.2016.7821010

Vancouver

Ozan EC, Riabchenko E, Kiranyaz S, Gabbouj M. A Vector Quantization Based k-NN Approach for Large-Scale Image Classification. julkaisussa 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA). IEEE. 2016 https://doi.org/10.1109/IPTA.2016.7821010

Author

Ozan, Ezgi Can ; Riabchenko, Ekaterina ; Kiranyaz, Serkan ; Gabbouj, Moncef. / A Vector Quantization Based k-NN Approach for Large-Scale Image Classification. 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA). IEEE, 2016.

Bibtex - Lataa

@inproceedings{83bb2ab51b9b457291797e360e6fb704,
title = "A Vector Quantization Based k-NN Approach for Large-Scale Image Classification",
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.",
author = "Ozan, {Ezgi Can} and Ekaterina Riabchenko and Serkan Kiranyaz and Moncef Gabbouj",
note = "EXT={"}Kiranyaz, Serkan{"}",
year = "2016",
month = "12",
doi = "10.1109/IPTA.2016.7821010",
language = "English",
isbn = "978-1-4673-8911-2",
publisher = "IEEE",
booktitle = "2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - A Vector Quantization Based k-NN Approach for Large-Scale Image Classification

AU - Ozan, Ezgi Can

AU - Riabchenko, Ekaterina

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

N1 - EXT="Kiranyaz, Serkan"

PY - 2016/12

Y1 - 2016/12

N2 - 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.

AB - 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.

U2 - 10.1109/IPTA.2016.7821010

DO - 10.1109/IPTA.2016.7821010

M3 - Conference contribution

SN - 978-1-4673-8911-2

BT - 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA)

PB - IEEE

ER -