DocumentCode :
3658883
Title :
Applying low rank representation based spatial pyramid matching in welding image classification
Author :
Aditya Narayanamoorthy;Xi Peng;Huajin Tang
Author_Institution :
Robotics Department, Institute for Infocomm Research, Singapore
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
208
Lastpage :
211
Abstract :
Spatial Pyramid Matching (SPM) and related methods have been found perform well in general image classification. A recently proposed improvement on this was LrrSPM, which used low rank representation of encode the SIFT descriptors, to achieve comparable recognition rates, with faster processing speeds. While image classification of this kind has been applied to many fields, an area where this has not been used is in industrial welding applications. This paper attempts to apply LrrSPM to welding image datasets, and show comparable classification results. It also proposes a cognitive architecture involving associative neural networks to perform classification on the images.
Keywords :
"Support vector machines","Welding","Neural networks","Feature extraction","Image classification","Conferences","Kernel"
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2015 IEEE 7th International Conference on
Print_ISBN :
978-1-4673-7337-1
Electronic_ISBN :
2326-8239
Type :
conf
DOI :
10.1109/ICCIS.2015.7274574
Filename :
7274574
Link To Document :
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