NDLI: Efficient complex ISAR object recognition using adaptive deep relation learning

Content Provider	IET Digital Library
Author	Liu, Chunsheng Wang, Zhongmei
Abstract	Complex inverse synthetic aperture radar (ISAR) object recognition is a critical and challenging problem in computer vision tasks. An efficient complex object recognition method for ISAR images is proposed based on adaptive deep relation learning. (i) An adaptive multimodal mechanism is proposed to greatly improve the multimodal sampling and transformation capabilities of convolutional neural networks and increase the resolutions of the feature maps. In particular, an adaptive ranking and selection strategy for related regions are proposed. (ii) Deep graphical learning is proposed to jointly estimate large numbers of heterogeneous attributes and leverage the relations among features and attributes. Extensive experiments performed on real-world datasets show that the proposed method outperforms other state-of-the-art methods.
Starting Page	185
Ending Page	191
Page Count	7
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 5, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0200
Journal	IET Computer Vision
Publisher Date	2020-02-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Deep Relation Learning Adaptive Multimodal Mechanism Adaptive Ranking Selection Strategy Complex Inverse Synthetic Aperture Radar Object Recognition Computer Vision Computer Vision And Image Processing Technique Computer Vision Tasks Critical Problem Deep Graphical Learning Efficient Complex ISAR Object Recognition ISAR Image Knowledge Engineering Technique Learning in AI Multimodal Sampling Neural Computing Technique Neural Nets Object Recognition Optical, Image And Video Signal Processing Radar Equipment Radar Imaging Recognition Method Related Region Synthetic Aperture Radar System And Application Transformation Capabilities
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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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