Please use this identifier to cite or link to this item:
Title: An ultra-specific image dataset for automated insect identification
Authors: Abeywardhana, L
Dangalle, C
Nugaliyadde, A
Mallawarachchi, Y
Keywords: Automated insect identifcation
Limited data
Tiger beetles
· Inter-class similarity
Issue Date: Jan-2022
Publisher: Springer Nature
Citation: Abeywardhana, Lakmini & Dangalle, Chandima & Nugaliyadde, Anupiya & Mallawarachchi, Yashas. (2022). An ultra-specific image dataset for automated insect identification. Multimedia Tools and Applications. 10.1007/s11042-021-11450-6.
Series/Report no.: Multimedia Tools and Applications (2022);81:3223–3251
Abstract: Automated identifcation of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from diferent sources, angles and on diferent scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing diferent unique patterns and features in images. The dataset was evaluated on diferent classifcation algorithms including deep learning models based on diferent approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classifcation algorithms. However transfer learning models using softmax classifer performed well on the current dataset. The tiger beetle classifcation can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classifcation algorithms to develop, to identify features which human eyes have not identifed.
ISSN: 1573-7721
Appears in Collections:Department of Information Technology
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

Files in This Item:
File Description SizeFormat 
  Until 2050-12-31
2.97 MBAdobe PDFView/Open Request a copy

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.