A Transductive Transfer Learning Method for Ship Target Recognition

Author

Zhiping Dan ; Nong Sang ; Ruolin Wang ; Yanfei Chen ; Xi Chen

Author_Institution

Inst. for Pattern Recognition & Artificial Intell., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2013

fDate

26-28 July 2013

Firstpage

418

Lastpage

422

Abstract

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.

Keywords

feature extraction; learning (artificial intelligence); object detection; object recognition; ships; 3D ship target projection; Hu moment; feature space; feature vector extraction; infrared image; ship target detection; ship target recognition; training ship sample; transductive transfer learning method; Feature extraction; Image recognition; Learning systems; Marine vehicles; Support vector machines; Target recognition; Training; image processing; machine learning; target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Graphics (ICIG), 2013 Seventh International Conference on

Conference_Location

Qingdao

Type

conf

DOI

10.1109/ICIG.2013.90

Filename

6643708