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

Abstract

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.

Attributed Relational SIFT-Based Regions Graph: Concepts and Applications

“…This section reviews the results previously obtained in [7]. The classification performance through Support Vector Machine (SVM) and Asymmetric Kernel Scaling (AKS) [57] over the standard OvA setup on low, medium, and high imbalanced image classification problems is tested, with art painting classification application [58].…”

“…Other systems, called Region-Based Image Retrieval [3] (RBIR), focus their attention on specific image regions instead of the entire content to extract features. In this paper, a graph structure for image representation, called Attributed Relational SIFT-based Regions Graph (ARSRG), is described, analyzed, and discussed with reference to previous works [4][5][6][7]. There are two main parts: examination of the structure, through the definition of its components, and the collection and analysis of the previously obtained results.…”

“…Kernel graph embedding [7]: The ARSRG is adopted to effectively tackle the imbalanced classification problem. The Kernel Graph Embedding on Attributed Relational Scale-Invariant Feature Transform-based Regions Graph (KGEARSRG) provides a vector-based image representation.…”