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
fDate :
31 July-4 Aug. 2005
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;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1555810
