A new weighted ARC-SC approach for leaf image recognition

Authors:
Zhi-De Zhi;Rong-Xiang Hu;Xiao-Feng Wang
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, Anhui, China,Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui, ...;Department of Automation, University of Science and Technology of China, Hefei, Anhui, China,Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui, ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui, China,Key Lab of Network and Intelligent Information Processing, Hefei University, Hefei, Ch ...
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 3
Cited 0

Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a novel feature extraction approach for plant leaf image recognition, which applies the arc length information to replace the Euclidean distance in traditional Shape Context (SC) method. Meanwhile, the shape is divided by the arc length into two parts, i.e. local and global feature. It can obtain the weighed cost of shape matching by combining the local with global feature. We compare this algorithm with the classic Inner-Distance Shape Context (IDSC) method on both Swedish and ICL leaf image dataset. Experimental results show that the proposed method achieves better performance compared with SC and IDSC methods.