Infrared object classification using decision tree based deep neural networks

In this work, we focus on the problem of infrared (IR) object classification by dividing the object appearance space hierarchically with a binary decision tree structure. Specially designed features of the object appearances make the binary decisions at each node of the tree. These features are extr...

Full description

Saved in:
Bibliographic Details
Published in2016 24th Signal Processing and Communication Application Conference (SIU) pp. 1913 - 1916
Main Authors Gundogdu, Erhan, Koc, Aykut, Alatan, A. Aydin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Biomedical imaging
classification
deep neural networks
Infrared object
Neural networks
Presses
Online AccessGet full text
DOI10.1109/SIU.2016.7496139

Cover

More Information
Summary:In this work, we focus on the problem of infrared (IR) object classification by dividing the object appearance space hierarchically with a binary decision tree structure. Specially designed features of the object appearances make the binary decisions at each node of the tree. These features are extracted using a fully connected deep neural network. At each node of the tree, we train individual deep CNNs such that each node specializes in its corresponding subspace. The proposed method is tested 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.
DOI:10.1109/SIU.2016.7496139