Earticle

초록

영어

Content-based medical image retrieval is an important tool for doctors in their daily activity. In this paper, we propose a novel image retrieval framework to combine visual concept and local features. To obtain visual semantic representation of the image, we first construct a graph model by feature distance and density similarity, and then a graph-based semi-supervised learning method is applied to get the membership degree of query images. Meanwhile, the dense SIFT feature of the image patches is extracted and described by bag of visual words as local features. Besides, we design a similarity measurement based on visual concept and local feature rather than using low level features only. We evaluate the proposed algorithm in ImageCLEFmed dataset. The results demonstrate that our method represents the visual semantic of images effectively, and compares favorably to state-of-the-art approaches based on single low level features in retrieval performance.

목차

Abstract
 1. Introduction
 2. Visual Semantic Extraction by Graph-based Semi-supervised Learning
  2.1 Learning Framework
  2.2 visual Concept Extract
 3. Local Features Extraction by Patch-based Visual Word
 4. Similarity Measurement
 5. Experiments
  5.1 Results on ImageCLEFmed 2009 Dataset
  5.2 Comparison with Other Graph-based Semi-supervised Learning Methods
  5.3 Results in Different d
 6. Conclusions and Future Work
 Acknowledgements
 References

키워드

저자

• Menglin Wu [ College of Electronics and Information Engineering, Nanjing University of Technology, School of Computer Science and Technology, Nanjing University of Science and Technology ]
• Quansen Sun [ School of Computer Science and Technology, Nanjing University of Science and Technology ]
• Jin Wang [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605