Normalization of Active Appearance Models for Fish Species Identification

Abstract EN

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.