Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images
This work addresses the need for efficient insect monitoring in agriculture or ecology, but it is incremental as it builds on previous detection methods.
The paper tackled the problem of automatically recognizing and counting moths in trap images under real-world conditions by adapting an SVM classifier with a multi-scale descriptor, achieving a classification accuracy of 95.8%.
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.