High-level feature extraction using SVM with walk-based graph kernel
We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segmented into a finite set of homogeneous segments and then represented as a segmentation graph where each vertex is a segment and edges connect adjacent segments. Given a set of features associated with each segment, we then obtain a positive definite kernel between images by comparing walks in the respective segmentation graphs, and image classification is carried out with an SVM based on this kernel. In a benchmark experiment on the MediaMill challenge problem, the mean average precision increased from 0.216 (baseline) to 0.341 when our method was utilized.