An ontology-based hybrid methodology for image synthesis and identification with convex objects

One of the core challenges in developing a computer system for machine learning is to make the system learn efficiently and effectively like a real human by grasping the domain knowledge exemplified by human experts. In this challenge, we have introduced a hybrid image synthesis model that can simul...

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Bibliographic Details
Published inThe imaging science journal Vol. 66; no. 8; pp. 492 - 501
Main Authors Sun, Nanfei, Lin, Jian (Denny), Wu, Michael Yu-Chi
Format Journal Article
LanguageEnglish
Published Taylor & Francis 17.11.2018
Subjects
feature extraction
image synthesis
object identification
Ontology
pattern recognition
solid geometries
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Summary:One of the core challenges in developing a computer system for machine learning is to make the system learn efficiently and effectively like a real human by grasping the domain knowledge exemplified by human experts. In this challenge, we have introduced a hybrid image synthesis model that can simulate one of the human's learning capabilities in the vision field - the ability to synthesize images of convex objects by identifying solid geometries and textures of specific objects using few photographs. We have incorporated an ontology-based, domain knowledge on solid geometries into our model to synthesize large number of training images with only a minimum number of input images. Our initial experiments have shown that our model has convincing improvements by demonstrating a substantially better FAR/FRR/EER results when it is compared with a smaller set of non-synthetic images.
ISSN:1368-2199
1743-131X
DOI:10.1080/13682199.2018.1532670