关键词: 图像分类, 核函数, 支持向量机

Abstract: This paper proposes a kernel function — hierarchical log-polar matching kernel which making use of the feature spatial information for building classification in remote sensing images. It extracts image local features, uses traditional clustering methods to quantize all feature vectors into several different types, and then partitions the image into multi-level increasingly fine log-polar “sub-regions (bins)”. By computing histograms of local features found inside each sub-region in each level, the weighted multi-scale histograms are formulated. By summing all weighted multi-level histograms of each feature vector, the final hierarchical log-polar kernel is established. The building classification is done with a support vector machine (SVM) trained by using the “one-versus-all” rule. The experimental results on Caltech-256 database and real remote sensing images demonstrate the efficiency and effec-tiveness of the proposed kernel.

Key words: image classification, kernel function, support vector machine (SVM)