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Title of Journal: Machine Vision and Applications

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Abbravation: Machine Vision and Applications

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Springer-Verlag

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10.1007/s11557-009-0641-1

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1432-1769

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Qualitative partbased models in contentbased ima

Authors: GuillaumeAlexandre Bilodeau Robert Bergevin
Publish Date: 2007/01/10
Volume: 18, Issue: 5, Pages: 275-287
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Abstract

A qualitative volumetric partbased model is proposed to improve the categorical invariance and viewpoint invariance in contentbased image retrieval and a novel twostep partcategorization method is presented to build it The method consists first in transforming parts extracted from a segmented contour primitive map and then categorizing the transformed parts using interpretation rules The first step allows noisy extracted parts to be transformed to the domain of a simple classifier The second step computes features of the transformed parts for categorization Contentbased image retrieval experiments using real images of complex multipart objects confirm that a model built from the categorized parts improves both the categorical invariance and the viewpoint invariance It does so by directly addressing the fundamental limits of lowlevel models

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