Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Texture Classification Using Wavelet-Domain BDIP and BVLC Features With WPCA Classifier

웨이브렛 영역의 BDIP 및 BVLC 특징과 WPCA 분류기를 이용한 질감 분류

  • Kim, Nam-Chul (School of Electronics Engineering, Kyungpook National University) ;
  • Kim, Mi-Hye (School of Electronics Engineering, Kyungpook National University) ;
  • So, Hyun-Joo (School of Electronics Engineering, Kyungpook National University) ;
  • Jang, Ick-Hoon (Department of Avionics Engineering, Kyungwoon University)
  • 김남철 (경북대학교 전자공학부) ;
  • 김미혜 (경북대학교 전자공학부) ;
  • 소현주 (경북대학교 전자공학부) ;
  • 장익훈 (경운대학교 항공전자공학과)
  • Received : 2011.02.26
  • Accepted : 2012.01.31
  • Published : 2012.03.25
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Abstract

In this paper, we propose a texture classification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features with WPCA (whitened principal component analysis) classifier. In the proposed method, the wavelet transform is first applied to a query image. The BDIP and BVLC operators are next applied to the wavelet subbands. Global moments for each subband of BDIP and BVLC are then computed and fused into a feature vector. In classification, the WPCA classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the query feature vector. Experimental results show that the proposed method yields excellent texture classification with low feature dimension for test texture image DBs.

본 논문에서는 웨이브렛 영역의 BDIP(block difference of inverse probabilities)와 BVLC(block variance of local correlation coefficients) 특징, 그리고 WPCA(whitened principal component analysis) 분류기를 이용한 질감 분류 방법을 제안한다. 제안된 방법에서는 먼저 질의 영상에 웨이브렛 변환을 적용한다. 그런 다음 웨이브렛 영역의 각 부대역에 BDIP와 BVLC 연산자를 적용한다. 이어서 각 BDIP, BVLC 부대역에 대하여 전역 통계치를 계산하고 그 결과들을 벡터화하여 특징 벡터로 사용한다. 분류 단계에서는 얼굴 인식에 주로 사용되는 WPCA를 분류기로 하여 질의 특징 벡터와 가장 유사한 학습 특징 벡터를 찾는다. 실험 결과 제안된 방법은 3가지의 실험 질감 영상 DB에 대하여 낮은 특징 벡터 차원으로 매우 우수한 질감 분류 성능을 보여준다.

Keywords

References

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