Maritime background infrared imagery classification based on histogram of oriented gradient and local contrast features
Received:December 19, 2019  Revised:August 20, 2020  download
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Author NameAffiliationE-mail
DONG Li-Li School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China donglili@dlmu.edu.cn 
ZHANG Tong School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China zhangtong_haishi@163.com 
MA Dong-Dong School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China  
XU Wen-Hai School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China  
Abstract:In the complex and changeable sea environment, when using infrared imaging technology to search and rescue small and medium targets on the sea surface, it is necessary to classify the collected original images in order to facilitate the subsequent target processing in different scenes. According to different environmental conditions, the sea infrared images are divided into five kinds of scenes. The training set images are extracted from two aspects: one is to divide an image into basic layer and detail layer by the Gaussian filter, and use improved histogram of oriented gradient (HOG) method to extract the features; the other is to extract features by calculating local contrast of images. The extracted feature vectors are fused and input into the classifier, and the test set images are classified by support vector machine (SVM). In this paper, a new feature descriptor combined with HOG and local contrast method (LCM) is used to classify the scene of sea infrared image. Compared with other methods, the results show that the accuracy of the improved method is 96.4%, which reflects the feasibility and effectiveness.
keywords:background classification  feature descriptors  histogram of oriented gradient (HOG)  local contrast method (LCM)  infrared images
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Copyright:《Journal of Infrared And Millimeter Waves》