Earticle

초록

영어

This paper introduces an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features. This research improves both feature selection and classifier partition of a CBIR system. This paper normalizes and descretizes the feature vectors. A pair-wise classifier is employed to improve the accuracy of classification. Results show high accuracy of proposed classifier in an image database.

목차

Abstract
 1. Introduction
 2. Image Feature Vector Selection
 3. Normalization of Feature Vector
 4. Descretization of Inputs
 5. Feature Selection
 6. Pair-Wise Classifier
 7. Determining the Most Erroneous Pair-Wise Categories
 8. Combination of Pair-Wise Classifiers
 9. Experiments and Results
 10. Conclusions
 References

키워드

저자

• Mahdi Bazarganigilani [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Ben Arko [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Ali Syed [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Camille Price [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Sandid Burki [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Belinda Fridey [ Faculty of Business and Management, Charles Sturt University, Australia ]
• Michael Baron [ Faculty of Business and Management, Charles Sturt University, Australia ]

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