Earticle

초록

영어

Image retrieval methods have been significantly developed in the last decade. The BOW (Bag-of-words) model lacks spatial information. Some methods stem from BOW approach which is recently extended to a vector aggregation model. Most of them are either too strict or too loose so that they are only effective in limited cases. In this study, we present a novel feature extraction method for image retrieval. We acquire the gradients features from the p.d.f (Probability density function) because of essentially representing the image. We construct the features by the histogram of the oriented p.d.f gradients via aggregation of the orientation codes. Then, we adopt the PCA (Principal component analysis) method to reduce the dimensionality of BOW. Furthermore, we introduce a novel and robust re-ranking method with the k-nearest neighbors. We estimate our method using various datasets. In the experiments on scene retrieval, the proposed method is efficient, and exhibits superior performances compared to the other existing methods.

목차

Abstract
 1. Introduction
 2. Summary of the Related Works
 3. Proposed Methods
  3.1 Oriented Probability Density Function Gradients
  3.2 Principal Component Analysis
  3.3 Aggregation of p.d.f Gradient Orientation Codes
  3.4 k-NN Re-Ranking
 4. Experiments and Analysis
  4.1 Results of k-NN Re-Ranking
  4.2 Comparisons to Other Methods
  4.3 Scalability for Large Datasets
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Pang Haibo [ School of Software Technology, Zhengzhou University, Zhengzhou, China, 450002 ]
• Liu Chengming [ School of Software Technology, Zhengzhou University, Zhengzhou, China, 450002 ]
• Zhao Zhe [ School of Software Technology, Zhengzhou University, Zhengzhou, China, 450002 ]
• Li Zhanbo [ School of Software Technology, Zhengzhou University, Zhengzhou, China, 450002 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605