NDLI: Varied channels region proposal and classification network for wildlife image classification under complex environment

Content Provider	IET Digital Library
Author	Guo, Yanhui Rothfus, Thomas A. Ashour, Amira S. Si, Lei Du, Chunlai Ting, Tih Fen
Abstract	A varied channels region proposal and classification network (VCRPCN) is developed based on a deep convolutional neural network (DCNN) and the characteristics of the animals appearing for automatic wildlife animal classification in camera trapped images, the architecture of the network is improved by feeding different channels into different components of the network to accomplish different aims, i.e. the animal images and their background images are employed in the region proposal component to extract region candidates for the animal's location, and the animal images combined with the region candidates are fed into the classification component to identify their categories. This novel architecture considers changes to the image due to the animals' appearances, and identifies potential animal regions in images and extracts their local features to describe and classify them. Five hundred low contrast animal images have been collected. All images have low contrast due to being acquired during the night. Cross-validation is employed to statistically measure the performance of the proposed algorithm. The experimental results demonstrate that in comparison with the well-known object detection network, faster R-CNN, the proposed VCRPCN achieved higher accuracy with the same dataset and training configuration with an average accuracy improvement of 21%.
Starting Page	585
Ending Page	591
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1042
Journal	IET Image Processing
Publisher Date	2019-11-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Animals Automatic Wildlife Animal Classification Background Image Camera Camera Trapped Image Classification Component Classification Network Computer Vision And Image Processing Technique Convolution Deep Convolutional Neural Network Different Aims Faster Region Convolutional Neural Network Feature Extraction Image Classification Image Segmentation Knowledge Engineering Technique Learning in AI Low Contrast Animal Image Neural Computing Technique Neural Nets Object Detection Object Detection Network Object Recognition Optical, Image And Video Signal Processing Potential Animal Region Region Candidates Region Proposal Component Varied Channel Region Proposal Wildlife Image Classification
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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