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

The Institute of Electronics and Information Engineers (대한전자공학회)
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On Optimizing Dissimilarity-Based Classifier Using Multi-level Fusion Strategies

다단계 퓨전기법을 이용한 비유사도 기반 식별기의 최적화

  • 김상운 (명지대학교 컴퓨터공학과) ;
  • 로버트 듀인 (네덜란드 델프트공과대학 전기 수학 컴퓨터학부)
  • Published : 2008.09.25
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Abstract

For high-dimensional classification tasks, such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in linear discriminant analysis-based methods for dimension reduction is what is known as the small sample size (SSS) problem. Recently, to solve the SSS problem, a way of employing a dissimilarity-based classification(DBC) has been investigated. In DBC, an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of the feature vector itself. In this paper, we propose a new method of optimizing DBCs using multi-level fusion strategies(MFS), in which fusion strategies are employed to represent features as well as to design classifiers. Our experimental results for benchmark face databases demonstrate that the proposed scheme achieves further improved classification accuracies.

얼굴인식 등과 같은 고차원 식별문제에서는 샘플패턴의 수가 패턴의 차원보다 작아지게 된다. 이러한 상황에서 차원을 축소하기위해 선형판별분석법을 적용할 경우, 희소성(Small Sample Size: SSS)문제가 발생한다. 최근, SSS 문제를 해결하기 위하여 비유사도에 기반 한 식별법(Dissimilarity-Based Classification: DBC)을 이용하는 방법이 검토되었다. DBC에서는 특징 벡터 대신에 학습 샘플들로부터 추출한 프로토타입들과의 비유사도를 측정하여 입력 패턴을 식별하는 방법이다. 본 논문에서는 비유사도 표현단계와 DBC 학습단계에서 퓨전기법을 중복 적용하는 다단계 퓨전기법(Multi-level Fusion Strategies: MFS)으로 DBCs를 최적화시키는 방법을 제안한다. 제안 방법을 벤취마크 얼굴영상 데이터베이스를 대상으로 실험한 결과, 식별률을 향상시킬 수 있음을 확인하였다.

Keywords

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