Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis

Abstract. We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the relative simplicity of small baseline tracking in image sequences to develop descriptors suitable for the more challenging task of wide baseline matching across significant viewpoint changes. The variations in the appearance of each feature are learned using kernel principal component analysis (KPCA) over the course of image sequences. An approximate version of KPCA is applied to reduce the computational complexity of the algorithms and yield a compact representation. Our experiments demonstrate robustness to wide appearance variations on non-planar surfaces, including changes in illumination, viewpoint, scale, and geometry of the scene.

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Jason Meltzer, Ming-Hsuan Yang, Rakesh Gupta, Stefano Soatto
• Keywords: Computer Vision, ECCV 2004, Feature Descriptors, Image Sequences, Significant Viewpoint Changes, Small Baseline Tracking, Wide Appearance Variations