전자공학회논문지CI (Journal of the Institute of Electronics Engineers of Korea CI)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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이미지 검색을 위한 Haar 웨이블릿 특징 검출자에 대한 연구

Study of the Haar Wavelet Feature Detector for Image Retrieval

  • 발행 : 2010.01.25
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 Haar 웨이브릿변환과 평균 박스필터에 기반을 둔 Haar 웨이브릿 특징 검출자를 제안한다. 원 영상을 Haar 웨이브릿 변환을 통해 분해하여 영상의 분산정보를 얻고 영상 식별을 위한 특징정보를 추출한다. 영역을 나타내는 주위영역들 중에 분산이 가장 큰 영역의 관심점을 검출하기 위하여 국부 분산정보를 비교하는 평균 박스필터를 적용하고 빠른 계산을 위한 적분영상 기법을 사용한다. Haar 웨이브릿 변환과 평균 박스필터를 이용하여 제안한 검출자는 밝기 변화, 스케일 변화, 영상의 회전에 민감하지 않는 특성을 제공할 수 있다. 실험결과는 제안한 방법이 적은 관심점을 사용하는 경우에도 기존의 DoG 검출자와 Harris corner 검출자에 비해 더 높은 repeatability와 효율성 그리고 매칭정확성을 달성할 수 있음을 보여준다.

This paper proposes a Haar Wavelet Feature Detector (HWFD) based on the Haar wavelet transform and average box filter. By decomposing the original image using the Haar wavelet transform, the proposed detector obtains the variance information of the image, making it possible to extract more distinctive features from the original image. For detection of interest points that represent the regions whose variance is the highest among their neighbor regions, we apply the average box filter to evaluate the local variance information and use the integral image technique for fast computation. Due to utilization of the Haar wavelet transform and the average box filter, the proposed detector is robust to illumination change, scale change, and rotation of the image. Experimental results show that even though the proposed method detects fewer interest points, it achieves higher repeatability, higher efficiency and higher matching accuracy compared with the DoG detector and Harris corner detector.

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