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Open AccessArticle

KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph

by Mario Manzo
Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, Italy
Current address: Via Nuova Marina, 59, 80133 Naples, Italy.
Mach. Learn. Knowl. Extr. 2019, 1(3), 962-973; https://doi.org/10.3390/make1030055
Received: 28 June 2019 / Revised: 26 August 2019 / Accepted: 27 August 2019 / Published: 28 August 2019
(This article belongs to the Section Learning)
In real world applications, binary classification is often affected by imbalanced classes. In this paper, a new methodology to solve the class imbalance problem that occurs in image classification is proposed. A digital image is described through a novel vector-based representation called Kernel Graph Embedding on Attributed Relational Scale-Invariant Feature Transform-based Regions Graph (KGEARSRG). A classification stage using a procedure based on support vector machines (SVMs) is organized. Methodology is evaluated through a series of experiments performed on art painting dataset images, affected by varying imbalance percentages. Experimental results show that the proposed approach consistently outperforms the competitors. View Full-Text
Keywords: kernel method; image classification; graph-based image representation kernel method; image classification; graph-based image representation
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Manzo, M. KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph. Mach. Learn. Knowl. Extr. 2019, 1, 962-973.

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