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- W2104431043 abstract "Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest-neighbor search. We establish the insensitivity of the signature to minor perturbation of graph structure due to noise, occlusion, or node split/merge. To accommodate large-scale occlusion, the DAG rooted at each nonleaf node of the query votes for model objects that share that part, effectively accumulating local evidence in a model DAG's topological subspaces. We demonstrate the approach with a series of indexing experiments in the domain of view-based 3D object recognition using shock graphs." @default.
- W2104431043 created "2016-06-24" @default.
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- W2104431043 date "2005-07-01" @default.
- W2104431043 modified "2023-09-24" @default.
- W2104431043 title "Indexing hierarchical structures using graph spectra" @default.
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- W2104431043 doi "https://doi.org/10.1109/tpami.2005.142" @default.
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