The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 컬러 영상 분류를 위한 블록 매칭 및 학습을 제안한다. 우리의 방법에서는 훈련 이미지가 작은 블록으로 분할됩니다. 테스트 이미지가 주어지면 작은 블록으로 분할되고 각 테스트 블록에 해당하는 평균 블록은 이웃 훈련 블록과 함께 계산됩니다. 우리의 방법은 테스트 이미지를 평균 블록과 테스트 블록 사이의 총 거리 합이 가장 짧은 클래스로 분류합니다. 또한 메모리 요구 사항을 줄이기 위한 학습 방법을 제안합니다. 실험 결과에 따르면 우리의 분류는 키포인트 백이 포함된 지원 벡터 머신과 같은 다른 분류기보다 성능이 뛰어납니다.
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부
Kazuki KONDO, Seiji HOTTA, "Color Image Classification Using Block Matching and Learning" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1484-1487, July 2009, doi: 10.1587/transinf.E92.D.1484.
Abstract: In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1484/_p
부
@ARTICLE{e92-d_7_1484,
author={Kazuki KONDO, Seiji HOTTA, },
journal={IEICE TRANSACTIONS on Information},
title={Color Image Classification Using Block Matching and Learning},
year={2009},
volume={E92-D},
number={7},
pages={1484-1487},
abstract={In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.},
keywords={},
doi={10.1587/transinf.E92.D.1484},
ISSN={1745-1361},
month={July},}
부
TY - JOUR
TI - Color Image Classification Using Block Matching and Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1484
EP - 1487
AU - Kazuki KONDO
AU - Seiji HOTTA
PY - 2009
DO - 10.1587/transinf.E92.D.1484
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
AB - In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.
ER -