Abstract
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.
Abstract (translated)
本文旨在开发一种在真实条件下从陷阱图像中进行蛾识别的自动算法。该方法使用我们以前的检测工作[1]并引入了一个改进的分类步骤。更准确地说，SVM分类器使用多尺度描述符（Curograminess Salfect（HCS）直方图）进行训练。该描述符对于照明变化是稳健的，并且能够以多尺度检测和描述目标昆虫的外部和内部轮廓。可以用一小组图像训练所提出的分类方法。定量评估表明，所提出的方法能够以比现有技术方法更高的准确度（率为95.8％）对昆虫进行分类。
URL
https://arxiv.org/abs/1809.06663
