OREGON STATE UNIVERSITY

You are here

Automated Insect Identification through Concatenated Histograms of Local Appearance Features

TitleAutomated Insect Identification through Concatenated Histograms of Local Appearance Features
Publication TypeConference Paper
Year of Publication2007
AuthorsLarios, N., H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, R. Correa, E. N. Mortensen, L. G. Shapiro, and T. G. Dietterich
Conference Name2007 IEEE Workshop on Applications of Computer Vision (WAC V '07)
Pagination26 - 26
Date Published02/2007
PublisherIEEE
Conference LocationAustin, TX
ISBN Number0-7695-2794-9
Abstract

This paper describes a fully automated stonefly-larvae classification system using a local features approach. It compares the three region detectors employed by the system: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the principal curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vectors for classification and compares the results using this methodology to that of Opelt [11] on three stonefly identification tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodology also outperforms the Opelt methodology in these tasks.

DOI10.1109/WACV.2007.13