Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/spie/target-classification-via-support-vector-machines-1cNv8ZgJeA
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- SPIE
- Copyright
- Copyright © 2000 Society of Photo-Optical Instrumentation Engineers
- ISSN
- 0091-3286
- eISSN
- 1560-2303
- DOI
- 10.1117/1.602417
- Publisher site
- See Article on Publisher Site
Abstract
The area of automatic target classification has been a difficult problem for many years. Many approaches involve extracting information from the imagery through a variety of statistical filtering and sampling techniques, resulting in a reduced dimension feature vector that is the input for a learning algorithm. We introduce the support vector machine (SVM) algorithm, which is a wide margin classifier that can provide reasonable results for sparse data sets and whose training speed can be nearly independent of feature vector size. Therefore, we can avoid the feature extraction step and process the images directly. The SVM algorithm has the additional features that there are few parameters to adjust and the solutions are unique for a given training set. We apply SVM to a vehicle classification problem and compare the results to standard neural network approaches. We find that the SVM algorithm gives equivalent or higher correct classification results compared to neural networks. © 2000 Society of Photo-Optical Instrumentation Engineers.
Journal
Optical Engineering – SPIE
Published: Mar 1, 2000
Keywords: support vector machine; target classification; neural network; computer vision