Dissertation/Thesis Abstract

Local and Deep Texture Features for Classification of Natural and Biomedical Images
by Oraibi, Zakariya Ahmed, Ph.D., University of Missouri - Columbia, 2019, 135; 22583771
Abstract (Summary)

In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.

Indexing (document details)
Advisor: Palaniappan, Kannappan
Commitee: Larsen, David R., Cheng, Jianlin, Bunyak, Filiz
School: University of Missouri - Columbia
Department: Computer Science
School Location: United States -- Missouri
Source: DAI-B 81/7(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Biomedical image analysis, Deep learning, Features, Machine learning, Pattern recognition, Texture dlassification
Publication Number: 22583771
ISBN: 9781392392836
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