Abstract
Parts-based recognition has been suggested for generalizing from few training views in categorization scenarios. In this paper we present the results of a comparative investigation of different feature types with regard to their suitability for category discrimination. So patches of gray-scale images were compared with SIFT descriptors and patches from the high-level output of a feedforward hierarchy related to the ventral visual pathway. We discuss the conceptual differences, resulting performance and consequences for hierarchical models of visual recognition. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Hasler, S., Wersing, H., & Körner, E. (2007). A comparison of features in parts-based object recognition hierarchies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4669 LNCS, pp. 210–219). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_22
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