Abstract
In this paper we address the comparison of two feature matching techniques
which can be integrated in the HMAX framework. This comparison involves the
originally proposed MAX technique 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. A second
investigation concentrates on comparing the often used hard vector quantization
technique and a soft matching score technique for the histogram creation. It
was found that the difference in performance is not significant and the scores are
often within their standard deviations. Aiming at an embedded implementation
such as in a surveillance system, computation power and memory (number of
dictionary features) are intrinsically limited, so that the histogram technique is
favored over the MAX technique.
Original language | English |
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Title of host publication | Proceedings of the 30th Symposium on Information Theory in the Benelux, May 28-29, 2009, Eindhoven, The Netherlands |
Editors | Tj. Tjalkens, F.M.J. Willems |
Place of Publication | Eindhoven |
Publisher | Technische Universiteit Eindhoven |
Pages | 169-176 |
ISBN (Print) | 978-90-386-1852-4 |
Publication status | Published - 2009 |