Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Effective Image Segmentation using a Locally Weighted Fuzzy C-Means Clustering

지역 가중치 적용 퍼지 클러스터링을 이용한 효과적인 이미지 분할

  • Alamgir, Nyma (School of Electrical Engineering, University of Ulsan) ;
  • Kim, Jong-Myon (School of Electrical Engineering, University of Ulsan)
  • Received : 2012.08.25
  • Accepted : 2012.11.24
  • Published : 2012.12.31
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Abstract

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

본 논문에서는 기존의 퍼지 클러스터링 기반 이미지 분할의 성능과 계산 효율을 개선하기 위해 퍼지 클러스터링의 목적 함수를 수정하는 이미지 분할 프레임워크를 제안한다. 제안하는 이미지 분할 프레임워크는 주변 픽셀들에 가중치를 부여함으로써 현재 센터 픽셀 연산을 위해 주변 픽셀들의 중요성을 고려하는 지역 가중치 적용 퍼지 클러스터링 기법을 포함한다. 이러한 가중치들은 각 멤버쉽들의 중요성을 표시하기 위해 현재 픽셀과 대응되는 각 주변 픽셀들 사이의 거리차에 의해 결정되어 지며, 이러한 프로세서는 향상된 클러스터링 성능을 보장한다. 제안하는 방법의 성능을 평가하기 위해 분할 계수, 분할 엔트로피, Xie-Bdni 함수, Fukuyzma-Sugeno 함수와 같은 네 가지 클러스터 유효성 함수를 이용하여 분석하였다. 모의실험 결과, 제안한 방법은 기존의 다른 퍼지 클러스터링 기법들보다 클러스터 유효성 함수들뿐만 아니라 분할과 조밀도 측면에서 우수한 성능을 보였다.

Keywords

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