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Semantic-oriented 3d shape retrieval using relevance feedback
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AI
DOI：10.1007/s00371-005-0341-z.png)
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Abstract
En 中文
Shape-based retrieval of 3D models has become an important challenge in computer graphics. Object similarity, however, is a subjective matter, dependent on the human viewer, since objects have semantics and are not mere geometric entities. Relevance feedback aims at addressing the subjectivity of similarity. This paper presents a novel relevance feedback algorithm that is based on supervised as well as unsupervised feature extraction techniques. It also proposes a novel signature for 3D models, the sphere projection. A Web search engine that realizes the signature and the relevance feedback algorithm is presented. We show that the proposed approach produces good results and outperforms previous techniques.
Keywords:
3D retrieval
search engine
relevance feedback
Journal
IF:
2.9
Papers:
4.6K
Citations:
6.5K
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