Semantic classification of scenes and places with omnidirectional vision

This paper presents a novel approach for semantic classification of scenes and places with omnidirectional images. The objective of scene classification is to segment and classify different regions in the image whereas place recognition assigns a single category to the entire image. In scene classification image regions are classified into categories floor, vertical planar surfaces and isolated objects e.g. furniture. The semantic segmentation extracts multiple heterogeneous visual features at the superpixel-level that are labeled by randomized decision trees. The place recognition relies on a global image representation (GIST) and two local densely extracted shape and appearance representations(HOG, dense SIFT). A support vector machine predicts place categories such as room, corridor, doorway and open space from these visual features.