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Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification

TitleHaar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification
Publication TypeConference Paper
Year of Publication2010
AuthorsLarios, N., B. Soran, L. G. Shapiro, G. Martinez-Munoz, J. Lin, and T. G. Dietterich
Conference Name2010 20th International Conference on Pattern Recognition (ICPR)2010 20th International Conference on Pattern Recognition
Pagination2624 - 2627
Date Published10/2010
PublisherIEEE
Conference LocationIstanbul, Turkey
ISBN Number978-1-4244-7542-1
KeywordsHaar-like features, machine learning, object-class recognition, Random Forests, SVM
Abstract

This paper proposes an image classification method based on extracting image features using Haar random forests and combining them with a spatial matching kernel SVM. The method works by combining multiple efficient, yet powerful, learning algorithms at every stage of the recognition process. On the task of identifying aquatic stonefly larvae, the method has state-of-the-art or better performance, but with much higher efficiency.

DOI10.1109/ICPR.2010.643