Fusing global and regional features for image classification

Authors:
Xiaohong Hu;Xu Qian
Affiliations:
School of Information and Management Science, Henan Agricultural University, Zhengzhou, China and School of Mechanical Electronic and Information Engineering, China University of Mining and Techno ...;School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 3
Cited 0

Multicriteria fuzzy decision-making problems based on vague set theory

Fuzzy Sets and Systems
Training fuzzy systems with the extended Kalman filter

Fuzzy Sets and Systems - Fuzzy systems
Normalized Cuts and Image Segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach to image classification based on the fusion of global and regional features, which are helpful to describe image semantics to classification, in which vague sets for positive and negative evidences is applied to analyze and optimize the decisions obtained by multi-classifiers. Through integrating two sides of multiple classification decisions, the classification is optimized and synthesized, thus the processing and results will be both powerful and stable. Experimental results show that the performance of the classification is greatly improved.