# Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images

arXiv: https://arxiv.org/abs/1809.06663 · HTML: https://mlsanity.com/paper/1809.06663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [A Case for Rejection in Low Resource ML Deployment](https://mlsanity.com/paper/2208.06359/) (2022)
- [BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India](https://mlsanity.com/paper/2304.00763/) (2023)

## Abstract (arXiv)

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.