書誌事項
- タイトル別名
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- Human-Directed Search of Three-Dimensional Mesh Models Based on Shape Similarity
- ヒト ノ キョウジ ニ モトヅク 3ジゲン メッシュ モデル ノ ケイジョウ ルイジ ケンサク
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As the popularity of three-dimensional (3D) geometric models has increased, so too has interest in methods to search 3D models based on their shape similarity. Two of the most difficult problems in finding shape similarity between 3D geometric models are a feature vector that succinctly describes the shape of the model and a method to compute the distance between a pair of feature vectors that reflects user preferences. We describe a human-directed 3D shape similarity search method that reflects, to some extent, the user preferences by using a learning classifier. Given an example 3D mesh model, the system first retrieves, as an initial guess, a set of models that are similar to the query based only on an unbiased mechanical measure of the distance (i.e., the Manhattan distance) between a set of feature vectors. The user then iteratively refines the query by tagging a subset of the retrieved models as being either similar or dissimilar. The system learns the user's preference using a learning classifier support vector machine (SVM), so that the distance values between the set of feature vectors are altered to reflect these preferences. Experimental results show that our method is capable of retrieving 3D models that better reflect the preferences of the user than the simple method using only the Manhattan distance.
収録刊行物
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- 映像情報メディア学会誌
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映像情報メディア学会誌 57 (8), 998-1007, 2003
一般社団法人 映像情報メディア学会
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詳細情報 詳細情報について
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- CRID
- 1390001205098070400
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- NII論文ID
- 110003671187
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- NII書誌ID
- AN10588970
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- ISSN
- 18816908
- 13426907
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- NDL書誌ID
- 6655513
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可