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Region-Based Hierarchical Image Matching

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Abstract

This paper presents an approach to region-based hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g., recursive embedding of regions). To this end, each image is represented by a tree of recursively embedded regions, obtained by a multiscale segmentation algorithm. This allows us to pose image matching as the tree matching problem. To overcome imaging noise, one-to-one, many-to-one, and many-to-many node correspondences are allowed. The trees are first augmented with new nodes generated by merging adjacent sibling nodes, which produces directed acyclic graphs (DAGs). Then, transitive closures of the DAGs are constructed, and the tree matching problem reformulated as finding a bijection between the two transitive closures on DAGs, while preserving the connectivity and ancestor-descendant relationships of the original trees. The proposed approach is validated on real images showing similar objects, captured under different types of noise, including differences in lighting conditions, scales, or viewpoints, amidst limited occlusion and clutter.

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Authors and Affiliations

  1. Computer Vision and Robotics Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL, 61801, USA

    Sinisa Todorovic & Narendra Ahuja

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  1. Sinisa Todorovic
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  2. Narendra Ahuja
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Correspondence to Sinisa Todorovic.

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Todorovic, S., Ahuja, N. Region-Based Hierarchical Image Matching. Int J Comput Vis 78, 47–66 (2008). https://doi.org/10.1007/s11263-007-0077-5

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  • DOI: https://doi.org/10.1007/s11263-007-0077-5

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