Table of Contents
ISRN Signal Processing
Volume 2011 (2011), Article ID 103293, 16 pages
Research Article

Normalization of Active Appearance Models for Fish Species Identification

Department of Information Processing, Tokyo Institute of Technology, Nagatsuta-Cho, Midori-Ku, Yokohama, Kanagawa 226-850, Japan

Received 12 January 2011; Accepted 7 February 2011

Academic Editors: C.-M. Kuo and L. Shen

Copyright © 2011 Charles-Henri Quivy and Itsuo Kumazawa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years, automatic visual coral reef monitoring has been proposed to solve the demerits of manual monitoring techniques. This paper proposes a novel method to reduce the computational cost of the standard Active Appearance Model (AAM) for automatic fish species identification by using an original multiclass AAM. The main novelty is the normalization of species-specific AAMs using techniques tailored to meet with fish species identification. Shape models associated to species-specific AAMs are automatically normalized by means of linear interpolations and manual correspondences between shapes of different species. It leads to a Unified Active Appearance Model built from species that present characteristic texture patterns. Experiments are carried out on images of fish of four different families. The technique provides correct classification rates up to 92% on 5 species and 84.5% on 12 species and is more than 4 times faster than the standard AAM on 12 species.