A Review on Deep Learning Approaches to Image Classification and Object Segmentation

Wu, Hao; Liu, Qi; Liu, Xiaodong

Abstract

Deep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Present proposed artificial neural networks and optimization skills have effectively achieved large-scale deep learnt neural networks showing better performance with deeper depth and wider width of networks. With the efforts in the present deep learning approaches, factors, e.g. network structures, training methods and training data sets are playing critical roles in improving the performance of networks. In this paper, deep learning models in recent years are summarized and compared with detailed discussion of several typical networks in the field of image classification, object detection and its segmentation. Most of the algorithms cited in this paper have been effectively recognized and utilized in the academia and industry. In addition to the innovation of deep learning algorithms and mechanisms, the construction of large-scale datasets and the development of corresponding tools in recent years have also been analyzed and depicted.

Citation

Wu, H., Liu, Q., & Liu, X. (2019). A Review on Deep Learning Approaches to Image Classification and Object Segmentation. Computers, Materials & Continua, 60(2), 575-597. https://doi.org/10.32604/cmc.2019.03595

• Journal Article Type: Article
• Acceptance Date: Dec 18, 2018
• Publication Date: 2019
• Deposit Date: Feb 14, 2019
• Publicly Available Date: Aug 9, 2019
• Journal: Computers, Materials & Continua
• Print ISSN: 1546-2218
• Electronic ISSN: 1546-2226
• Publisher: Tech Science Press
• Peer Reviewed: Peer Reviewed
• Volume: 60
• Issue: 2
• Pages: 575-597
• DOI: https://doi.org/10.32604/cmc.2019.03595
• Keywords: Deep learning, image classification, object detection, object segmentation, convolutional neural network
• Public URL: http://researchrepository.napier.ac.uk/Output/1579400
• Contract Date: Feb 14, 2019