Accession Number:

AD1105032

Title:

Understanding State-of-the-Art Material Classification through Deep Visualization

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

ENGINEER RESEARCH AND DEVELOPMENT CENTER VICKSBURG MSMISSISSIPPI STATE UNIV STARKVILLE

Personal Author(s):

• Donovan, Jordan T.

Report Date:

2020-07-31

Pagination or Media Count:

100

Abstract:

Neural networks NNs excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neurons contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.

Descriptors:

• computer vision
• machine learning
• artificial neural networks
• automata theory
• artificial intelligence software
• bayesian networks
• computers
• supervised machine learning
• engineering
• image recognition
• operations research
• pattern recognition
• artificial intelligence
• computer science
• image processing
• image segmentation
• information systems
• literature surveys
• operating systems
• probability

Subject Categories:

• Cybernetics

Distribution Statement:

[A, Approved For Public Release]