Qualitative part-based models in content-based image retrieval

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• 作者：Guillaume-Alexandre Bilodeau and Robert Bergevin
• 关键词：Part ; based model ; Image retrieval ; Qualitative volumetric primitives ; Shape categorization
• 刊名：Machine Vision and Applications
• 出版年：2007
• 出版时间：October, 2007
• 年：2007
• 卷：18
• 期：5
• 页码：275-287
• 全文大小：790 KB
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  Image Processing and Computer Vision
  Communications Engineering and Networks
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1769

文摘

A qualitative, volumetric part-based model is proposed to improve the categorical invariance and viewpoint invariance in content-based image retrieval, and a novel two-step part-categorization 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. Content-based image retrieval experiments using real images of complex multi-part 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 low-level models.