Abstract
We present an example-based algorithm for detecting objects in images by integrating component-based classifiers, which automaticaly select the best feature for each classifier and are combined according to the AdaBoost algorithm. The system employs a soft-margin SVM for the base learner, which is trained for all features and the optimal feature is selected at each stage of boosting. We employed two features such as a histogram-equalization and an edge feature in our experiment. The proposed method was applied to the MIT CBCL pedestrian image database, and 100 sub-regions were extracted from each image as local-features. The experimental results showed fairly good classification ratio with selecting sub-regions, while some improvement attained by combining the two features, histogram-equalization and edge. However, the combination of features could to select good local-features for base learners.
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© 2005 Springer-Verlag Berlin Heidelberg
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Nishida, K., Kurita, T. (2005). Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_3
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DOI: https://doi.org/10.1007/11494683_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
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