This paper proposes a Lepidoptera insect image recognition method based on extracting image features using superpixels segmentation, encoding the features with Locality-constrained Linear Coding (LLC), aggregating codes with max pooling, and then classifying them with classification and regression tree (CART). This method used the natural scale color patterns on the insect wings as the basis for recognition, which can avoid the complicated chemical processing needed for venation based recognition. The method is tested in a dataset including 579 image samples from ten species of Lepidoptera species, and the recognition error rate is below 5%. The method also exhibits good performance with respect to time cost. The experimental results suggest that on the task of recognizing Lepidoptera species, the proposed method has state-of-the-art performance with high efficiency.
You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither BioOne nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the BioOne website.
Vol. 89 • No. 3