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OBJECT CLASSIFICATION IN INFRARED IMAGES USING DEEP REPRESENTATIONS

2016-09-29
Gündoğdu, Erhan
Koç, AYKUT
Alatan, Abdullah Aydın
In this study, we address the problem of infrared (IR) object classification that divides the object appearance space hierarchically with a binary decision tree structure. Binary decisions are made by using the special features of the object appearances. These features are extracted using a fully connected deep neural network learnt by training samples. At each node of the tree, we train individual deep CNNs such that each node specializes in its corresponding subspace. The proposed classification algorithm is evaluated in our generated dataset, which consists of IR targets collected from different video records obtained from different IR sensors (both midwave and longwave) and taken from real world field. The generated dataset consists of four different class labels as ship/boat, tank, plane and helicopter containing a total of 16K samples. Using the proposed tree-based classifier, we observe a favourable performance increase in our dataset against a single deep CNN classifier.