title: Automatic segmentation and classification of outdoor images using neural networks.
abstract: The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perception is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.
authors.affiliation: Advanced Computing Research Centre, University of Bristol, UK. Neill.Campbell@bristol.ac.uk
authors.forename: N W
authors.initials: NW
authors.lastname: Campbell
authors.forename: B T
authors.initials: BT
authors.lastname: Thomas
authors.forename: T
authors.initials: T
authors.lastname: Troscianko
mesh_terms: D001331:Automation; D002965:Classification; D003116:Color; D004784:Environmental Monitoring / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D015203:Reproducibility of Results