Outdoor scene classification by a neural tree-based approach
This paper describes a Neural Tree (NT) based system for outdoor scene classification. A new NT classifier with backtracking capabilities is employed at different levels of the system architecture. First, it is used to obtain a rough interpretation of the scene by classifying each image pixel into multiple classes of static background objects, e.g. road, sky, vegetation, or into one generic class representing moving objects, e.g, vehicles, pedestrians. Then it is applied to obtain a more accurate scene interpretation by classifying all detected mobile objects into multiple classes, e.g. cars, lorries, buses, and also estimating their pose. Experiments have been performed on a large set of optical and infrared images. System performances are tested on both clean and noisy data, and comparative studies with other classifiers (i.e, a multi layer perceptron, a binary decision tree, a standard NT and a bank of neural networks) and with other scene classification methods are carried out.
neural trees infrared images change detection invariant moments object recognition outdoor scene understanding