Journal article
Many-to-many feature matching using spherical coding of directed graphs
COMPUTER VISION - ECCV 2004, PT 1, Vol.3021, pp.322-335
Lecture Notes in Computer Science
01 Jan 2004
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge-weighted directed graph. The algorithm was based on a metric-tree representation of labeled graphs and their metric embedding into normed vector spaces, using the embedding algorithm of Matousek [13]. However, the method was limited by the fact that two graphs to be matched were typically embedded into vector spaces with different dimensionality. Before the embeddings could be matched, a dimensionality reduction technique (PCA) was required, which was both costly and prone to error. In this paper, we introduce a more efficient embedding procedure based on a spherical coding of directed graphs. The advantage of this novel embedding technique is that it prescribes a single vector space into which both graphs are embedded. This reduces the problem of directed graph matching to the problem of geometric point matching, for which efficient many-to-many matching algorithms exist, such as the Earth Mover's Distance. We apply the approach to the problem of multi-scale, view-based object recognition, in which an image is decomposed into a set of blobs and ridges with automatic scale selection.
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Details
- Title
- Many-to-many feature matching using spherical coding of directed graphs
- Creators
- M F DemirciA ShokoufandehS DickinsonY KeselmanL Bretzner
- Contributors
- T Pajdla (Editor)J Matas (Editor)
- Publication Details
- COMPUTER VISION - ECCV 2004, PT 1, Vol.3021, pp.322-335
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991019170581704721
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- Domestic collaboration
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods