Improving shape retrieval by spectral matching and meta similarity
We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise se...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 19(2010), 5 vom: 02. Mai, Seite 1319-27 |
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| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2010
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deformations. This allows both shape matching and retrieval. We also introduce a shape meta-similarity measure that agglomerates pairwise shape similarities and improves the retrieval accuracy. When applied to the MPEG-7 shape dataset in conjunction with the proposed geometric matching scheme, we obtained a retrieval rate of 92.5% |
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| Beschreibung: | Date Completed 29.11.2010 Date Revised 23.07.2010 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2010.2040448 |