Abstract
Matching two images by mapping image features play a fundamental role in many computer vision task. Due to noisy nature of feature extraction, establishing a one-to-one matching of features may not always be possible. Although many-to-many matching techniques establishes the desired multi map between features, they ignore the spatial structure of the nodes. In this paper, we propose a novel technique that utilizes both the individual node features and the clustering information of nodes for image matching where image features are represented as hierarchically well-separated trees (HSTs). Our method uses the fact that non-leaf nodes of an HST represent a constellation of nodes in the original image and obtains a matching by finding a mapping between non-leaf nodes among the two HSTs. Empirical evaluation of the method on an extensive set of recognition tests shows the robustness and efficiency of the overall approach.
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Osmanlıoğlu, Y., Shokoufandeh, A. (2015). Multi-layer Tree Matching Using HSTs. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_20
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DOI: https://doi.org/10.1007/978-3-319-18224-7_20
Publisher Name: Springer, Cham
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