DocumentCode
445800
Title
Automatic target recognition using new support vector machine
Author
Casasent, David ; Wang, Yu-Chiang
Author_Institution
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
1
fYear
2005
fDate
31 July-4 Aug. 2005
Firstpage
84
Abstract
A hierarchical classifier using a new SVRDM (support vector representation and discrimination machine) is proposed for automatic target recognition. An accuracy and distance-based method is used to design a hierarchical classifier. Our SVRDM hierarchical classifier has the ability to reject unseen non-object classes and clutter inputs. Uses of both iconic and spatial frequency domain features are considered. Initial recognition and rejection test results on infrared (IR) data are excellent.
Keywords
image classification; object recognition; support vector machines; target tracking; SVRDM hierarchical classifier; automatic target recognition; distance-based method; infrared data; support vector representation-and-discrimination machine; Classification tree analysis; Feature extraction; Frequency domain analysis; Kernel; Noise reduction; Support vector machine classification; Support vector machines; Target recognition; Testing; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN
0-7803-9048-2
Type
conf
DOI
10.1109/IJCNN.2005.1555810
Filename
1555810
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