In this paper we consider an object categorization system using local HMAX features. Two feature matching techniques are compared: the MAX technique, originally proposed in the HMAX framework, and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX technique with 5-15% for small dictionaries up to 500-1,000 features, favoring this technique for embedded (surveillance) applications. Additionally, we have evaluated the influence of interest point operators in the system. A first experiment analyzes the effect of dictionary creation and has showed that random dictionaries outperform dictionaries created from Hessian-Laplace points. Secondly, the effect of operators in the dictionary matching stage has been evaluated. Processing all image points outperforms the point selection from the Hessian-Laplace operator.
|Title of host publication||Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2009), 28 September - 2 October 2009, Bordeaux, France|
|Editors||J. Blanc-Talon, W. Philips, D. Popescu, P. Scheunders|
|Place of Publication||Berlin|
|Publication status||Published - 2009|
|Name||Lecture Notes in Computer Science|