Getto, Roman ; Kuijper, Arjan ; Fellner, Dieter W. (2017)
Unsupervised 3D object retrieval with parameter-free hierarchical clustering.
Yokohama, Japan
doi: 10.1145/3095140.3095147
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In 3D object retrieval, additional knowledge like user input, classification information or database dependent configured parameters are rarely available in real scenarios. For example, meta data about 3D objects is seldom if the objects are not within a well-known evaluation database. We propose an algorithm which improves the performance of unsupervised 3D object retrieval without using any additional knowledge. For the computation of the distances in our system any descriptor can be chosen; we use the Panorama-descriptor. Our algorithm uses a precomputed parameter-free agglomerative hierarchical clustering and combines the information of the hierarchy of clusters with the individual distances to improve a single object query. Additionally, we propose an adaption algorithm for the cases that new objects are added frequently to the database. We evaluate our approach with 6 databases including a total of 13271 objects in 481 classes. We show that our algorithm improves the average precision in an unsupervised scenario without any parameter configuration.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Getto, Roman ; Kuijper, Arjan ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | Unsupervised 3D object retrieval with parameter-free hierarchical clustering |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Ort: | New York |
| Verlag: | ACM |
| Buchtitel: | CGI'17 : Proceedings of the Computer Graphics International Conference |
| Veranstaltungsort: | Yokohama, Japan |
| DOI: | 10.1145/3095140.3095147 |
| URL / URN: | https://doi.org/10.1145/3095140.3095147 |
| Kurzbeschreibung (Abstract): | In 3D object retrieval, additional knowledge like user input, classification information or database dependent configured parameters are rarely available in real scenarios. For example, meta data about 3D objects is seldom if the objects are not within a well-known evaluation database. We propose an algorithm which improves the performance of unsupervised 3D object retrieval without using any additional knowledge. For the computation of the distances in our system any descriptor can be chosen; we use the Panorama-descriptor. Our algorithm uses a precomputed parameter-free agglomerative hierarchical clustering and combines the information of the hierarchy of clusters with the individual distances to improve a single object query. Additionally, we propose an adaption algorithm for the cases that new objects are added frequently to the database. We evaluate our approach with 6 databases including a total of 13271 objects in 481 classes. We show that our algorithm improves the average precision in an unsupervised scenario without any parameter configuration. |
| Freie Schlagworte: | 3D Object retrieval, Classifications, Clustering |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 05 Mai 2020 14:59 |
| Letzte Änderung: | 04 Feb 2022 12:39 |
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