A Transductive Transfer Learning Method for Ship Target Recognition

Ship target recognition in infrared image remains a difficult problem, due to the projection or silhouette of a three-dimensional ship target being variable in shape, orientation and scale to make its recognizability unstable. In this paper, a transductive transfer learning framework is proposed to...

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Bibliographic Details
Published in2013 Seventh International Conference on Image and Graphics pp. 418 - 422
Main Authors Zhiping Dan, Nong Sang, Ruolin Wang, Yanfei Chen, Xi Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2013
Subjects
Feature extraction
image processing
Image recognition
Learning systems
machine learning
Marine vehicles
Support vector machines
Target recognition
Training
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Summary:Ship target recognition in infrared image remains a difficult problem, due to the projection or silhouette of a three-dimensional ship target being variable in shape, orientation and scale to make its recognizability unstable. In this paper, a transductive transfer learning framework is proposed to solve the problem. Hu moments is firstly extracted as feature vectors of target. Then the transductive transfer learning method is used to find the common parameters between the feature spaces of the training ship samples and the detected ship targets, and transfer the similar knowledge from those data with different distributions. According to the experiment result in simulation infrared images, it shows that the ship targets can be recognized highly and reliably by our proposed framework. It demonstrates the robustness and effectiveness of our method for infrared images.
DOI:10.1109/ICIG.2013.90