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Adaptive hierarchical architecture for visual recognition
Faculty of Informatics - Papers (Archive)
  • Fok Hing Chi Tivive, University of Wollongong
  • Abdesselam Bouzerdoum, University of Wollongong
  • Son Lam Phung, University of Wollongong
  • Khan M Iftekharuddin, University of Memphis
Publication Date
Publication Details
Tivive, F., Bouzerdoum, A., Phung, S. & Iftekharuddin, K. M. (2010). Adaptive hierarchical architecture for visual recognition. Applied Optics, 49 (10), B1-B8.
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 filters 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.
Citation Information
Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Son Lam Phung and Khan M Iftekharuddin. "Adaptive hierarchical architecture for visual recognition" (2010)
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