The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Multi-class Feedback Algorithm for Region-based Image Retrieval

영역 기반 영상 검색을 위한 다중클래스 피드백 알고리즘

  • Published : 2006.08.01
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Abstract

In this paper, we propose a new relevance feedback algorithm using Probabilistic Neural Networks(PNN) while supporting multi-class learning. Then, to validate the effectiveness of our feedback approach, we incorporate the proposed algorithm into our region-based image retrieval tool, FRIP(Finding Regions In the Pictures). In our feedback approach, there is no need to assume that feature vectors are independent, and as well as it allows the system to insert additional classes for detail classification. In addition, it does not have a long computation time for training because it only has four layers. In the PNN classification process, we store the user's entire past feedback actions as a history in order to improve performance for future iterations. By using a history, our approach can capture the user's subjective intension more precisely and prevent retrieval performance errors which originate from fluctuating or degrading in the next iteration. The efficacy of our method is validated using a set of 3000 images derived from a Corel-photo CD.

본 논문에서는 영역기반 영상검색의 성능 향상을 위한 피드백 알고리즘으로 다중 클래스를 갖는 확률적 신경망(Probabilistic Neural Networks)을 이용한 방법론을 제안하고 이를 영역기반 영상 검색 시스템인 FRIP(Finding Regions In the Pictures) 시스템에 적용하였다. 본 논문에서 제안하는 피드백 알고리즘은 특정 벡터가 독립적이라는 가정을 할 필요가 없으며 보다 상세한 분류를 위해 추가적인 클래스들을 추가할 수 있도록 허용하고 있다. 또한 단지 4개 층(layer)만을 가지고 있음으로 학습을 위한 계산시간이 적게 든다는 장점이 있다. 추가적으로 다음단계에서의 성능 향상을 위해 분류 단계에서 사용자의 이전 피드백 행동을 모두 히스토리(history)로 모두 기억시켜 놓고 다음 단계를 위한 가중치 학습을 위해 사용하도록 한다. 히스토리를 사용함으로써 제안하는 알고리즘은 사용자의 주관적 의도를 보다 정확하게 파악 할 수 있을 뿐만 아니라 학습을 위해 이전 단계만을 사용 했을 때 발생할 수 있는 성능 감소를 막을 수 있다. 본 논문에서는 Corel-photo CD에서 3000장의 자연 영상을 무작위로 추출하여 기존의 방법론들과 제안하는 방법론의 성능을 측정하여 본 논문에서 제안하는 방법론이 성능이 우수함을 증명하였다.

Keywords

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