  This paper aims at developing an automatic algorithm for moth recognition
from trap images in real-world conditions. This method uses our previous work
for detection [1] and introduces an adapted classification step. More
precisely, SVM classifier is trained with a multi-scale descriptor, Histogram
Of Curviness Saliency (HCS). This descriptor is robust to illumination changes
and is able to detect and to describe the external and the internal contours of
the target insect in multi-scale. The proposed classification method can be
trained with a small set of images. Quantitative evaluations show that the
proposed method is able to classify insects with higher accuracy (rate of
95.8%) than the state-of-the art approaches.
