Research of animals image semantic segmentation based on deep learning

• Published in: Concurrency and computation Vol. 32; no. 1
• Main Authors: Liu, Shouqiang, Li, Miao, Li, Min, Xu, Qingzhen
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.01.2020
• Subjects: Animals; Caffe; Conditional random fields; CRF‐RNN; Deep learning; FCN; Image processing; Image segmentation; image semantic segmentation; Machine learning; Recurrent neural networks; Semantic segmentation; Semantics; Training
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.4892

Summary It is imperative for us to develop the technology of image semantic segmentation with the increasing demand in the image processing. Nowadays, the development of deep learning is of great significance to the improvement of image segmentation. Furthermore, the paper discussed the relationship between image semantic segmentation and animal image research based on the actual situation, and found that animal image processing technology plays a more important role in the field of protecting precious animals. The end‐to‐end network training of this paper is consisted of Fully Convolutional Network (FCN) for the front end and Conditional Random Fields as Recurrent Neural Networks (CRF‐RNN) for the back end via comparing a variety of research methods. The experiments achieved desired outcome for the semantic segmentation of animal images by utilizing Caffe deep learning framework and explained the implementation details from the aspects of training and testing.