An approach of multi-level semantics abstraction

  • Authors:
  • Hongli Xu;De Sun Zhijie Xu

  • Affiliations:
  • Dept. of Computer Science & Technology, Beijing Jiaotong Univ., Beijing, China;Dept. of Computer Science & Technology, Beijing Jiaotong Univ., Beijing, China

  • Venue:
  • KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
  • Year:
  • 2005

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Abstract

Semantics-based image retrieval is a challenging problem. In this paper, we propose an approach for image semantics abstraction, which constructs a multi-level semantics tree based on human subject and train hierarchical semantic classifier. According to our method, image features are selected by using priori knowledge. Then, those images are classified in every level by the classifier based on support vector machines (SVM). The SVM classifiers learn the semantics of specified classes from a training database of image. Experiments show that we can abstract multi-level semantics from image database by using less low-level features.