Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach

Abstract

Difference of Gaussians (DoG) scale-space for an image is a significant way to generate features for object detection and classification. While applying DoG scale-space features for object detection/classification, we face two inevitable issues: dealing with high dimensional data and selecting/weighting of proper scales. The scale selection process is mostly ad-hoc to date. In this paper, we propose a multiple kernel learning (MKL) method for both DoG scale selection/weighting and dealing with high dimensional scale-space data. We design a novel shift invariant kernel function for DoG scale-space. To select only the useful scales in the DoG scale-space, a novel framework of MKL is also proposed. We utilize a 1-norm support vector machine (SVM) in the MKL optimization problem for sparse weighting of scales from DoG scale-space. The optimized data-dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle. With a 2-norm SVM this learned kernel is applied to a challenging detection problem in oil sand mining: to detect large lumps in oil sand videos. We tested our method on several challenging oil sand data sets. Our method yields encouraging results on these difficult-to-process images and compares favorably against other popular multiple kernel methods.