Abstract: 【Aim】 This study aims to explore the method to realize the automatic insect image recognition based on computer vision technology. 【Methods】 The captured insect image was first preprocessed to remove the background, and were segmented into two pairs of wings, and the position of the wings was calibrated. Then the calibrated wings were divided into several regions along radial and angular directions. WLD (Weber Local Descriptor) and HoC (Histogram of Color) features were extracted and normalized in each region. The WLD features are extracted on grayscale image, reflecting the local texture feature of wing images. HoC features were extracted on HSI (Hue, Saturation, Intensity) color space, reflecting the color distribution information of the region. The WLD features and HoC features from all the regions of two pairs of wings were concatenated into a feature vector of the insect image. The feature vectors extracted from the insect image samples in training set were used to train the SVMs (Support Vector Machines) which were finally used to classify lepidopteran insects. 【Results】 The proposed method was tested in a database with 576 images and the standalone prediction accuracy was as high as 100%, and the system also demonstrated ideal time performance, good robustness and high stability. 【Conclusion】 The experimental results prove that the combination of WLD and HoC is an effective method for insect image feature description.

Key words: Insect, Lepidoptera, image recognition, Histogram of Color (HoC), Weber Local Descriptor (WLD), Support Vector Machine (SVM)