Abstract
Patch based approaches have recently shown promising results for the recognition of visual object classes. This paper investigates the role of different properties of patches. In particular, we explore how size, location and nature of interest points influence recognition performance. Also, different feature types are evaluated. For our experiments we use three common databases at different levels of difficulty to make our statements more general. The insights given in the conclusion can serve as guidelines for developers of algorithms using image patches.
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Teynor, A., Rahtu, E., Setia, L., Burkhardt, H. (2006). Properties of Patch Based Approaches for the Recognition of Visual Object Classes. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_29
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DOI: https://doi.org/10.1007/11861898_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
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