title: Adaptive hierarchical architecture for visual recognition.
abstract: We propose a new hierarchical architecture for visual pattern classification. The new architecture consists of a set of fixed, directional filters and a set of adaptive filters arranged in a cascade structure. The fixed filters are used to extract primitive features such as orientations and edges that are present in a wide range of objects, whereas the adaptive filters can be trained to find complex features that are specific to a given object. Both types of filter are based on the biological mechanism of shunting inhibition. The proposed architecture is applied to two problems: pedestrian detection and car detection. Evaluation results on benchmark data sets demonstrate that the proposed architecture outperforms several existing ones.
authors.affiliation: School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
authors.forename: Fok H C
authors.initials: FH
authors.lastname: Tivive
authors.forename: Abdesselam
authors.initials: A
authors.lastname: Bouzerdoum
authors.forename: Son Lam
authors.initials: SL
authors.lastname: Phung
authors.forename: Khan M
authors.initials: KM
authors.lastname: Iftekharuddin
mesh_terms: D003199:Computer Systems; D006801:Humans; D055620:Optical Phenomena; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual