Earticle

초록

영어

Automatic image annotation (AIA) is expected to be a promising way to improve the performance of content-based image retrieval (CBIR). However, current image annotation results are always incomplete and noisy, and far from practical usage. In this paper, we incorporate semantic annotations into CBIR via query expansion scheme to improve retrieval accuracy. In the proposed method, semantic annotations of test images are obtained using a visual nearest-neighbor-based annotation model. And both visual features and annotation keywords are used to represent images. The similarity between two images is determined by their visual similarity and semantic similarity. The method is evaluated on the well-known Pascal VOC 2007 dataset using standard performance evaluation metric. The experimental results indicate that the performance of CBIR can be improved by incorporating semantic annotation via query expansion.

목차

Abstract
 1. Introduction
 2. Proposed Approach
  2.1. Visual Feature Extraction and Automatic Image Annotation
  2.2. Query Expansion Using Annotations for CBIR
 3. Experiments and Discussion
  3.1. Experimental Data and Performance Metrics
  3.2. Results and Comparisons
 4. Conclusions
 References

키워드

저자

• Guoqing Xu [ School Of Computer and Information Engineering Nanyang Institute of Technology, Nanyang China ]
• Jian Li [ School Of Computer and Information Engineering Nanyang Institute of Technology, Nanyang China ]
• Chunyu Xu [ School Of Computer and Information Engineering Nanyang Institute of Technology, Nanyang China ]
• Qi Wang [ School Of Computer and Information Engineering Nanyang Institute of Technology, Nanyang China ]

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