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

Author

Larios, N. ; Soran, B. ; Shapiro, L.G. ; Martínez-Munoz, G. ; Lin, J. ; Dietterich, T.G.

Author_Institution

Univ. of Washington, Seattle, WA, USA

fYear

2010

fDate

23-26 Aug. 2010

Firstpage

2624

Lastpage

2627

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.

Keywords

biology computing; feature extraction; image classification; image matching; support vector machines; Haar random forest features; SVM spatial matching kernel; aquatic stonefly larvae; image classification method; image feature extraction; stonefly species identification; support vector machines; Feature extraction; Histograms; Image color analysis; Insects; Kernel; Support vector machines; Training; Haar-like features; Random Forests; SVM; machine learning; object-class recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (ICPR), 2010 20th International Conference on

Conference_Location

Istanbul

ISSN

1051-4651

Print_ISBN

978-1-4244-7542-1

Type

conf

DOI

10.1109/ICPR.2010.643

Filename

5595990

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2481700