芸術科学会論文誌 投稿用カバーシート ■ 論文種類(以下のうちから一つ選択) ・原著論文 フルペーパー ■ 論文分野(1)〜3)のうちから一つ選択) 2) 科学系分野 ■ カテゴリ(1個以上選択) a-1) CG技術(モデリング) d) その他( 情報検索 ) ■ 該当特集(以下のうちから一つ選択) ・一般論文 ■ 論文題名(英文) Supervised Learning of Salient 2D Views of 3D Models ■ 著者名(和文、英文) Hamid Laga Masayuki Nakajima ■ 著者所属(和文、英文) Global Edge Institute, Tokyo Institute of Technology Computer Science Department, Tokyo Institute of Technology ■ 著者e-mail hamid@img.cs.titech.ac.jp ■ 連絡担当者の氏名、住所、所属、電話、Fax、e-mail Hamid Laga, 東京都目黒区大岡山2−12−1、S6-9 Tel./Fax: 03-5734-3776, email: hamid@img.cs.titech.ac.jp ■ 論文概要(和文400字程度、英文100ワード程度) We introduce a new framework for the automatic selection of the best views of 3D models based on the assumption that models belonging to the same class of shapes share the same salient features. The main issue is learning these features. We propose an algorithm for computing these features and their corresponding saliency value. At the learning stage, a large set of features are computed from every model and a boosting algorithm is applied to learn the classification function in the feature space. AdaBoost learns a classifier that relies on a small subset of the features with the mean of weak classifiers, and provides an efficient way for feature selection and combination. Moreover it assigns weights to the selected features which we interpret as a measure of the feature saliency within the class. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark show the suitability of the approach to 3D shape classification and best-view selection for online visual browsing of 3D data collections. ■ キーワード(和文5個程度、英文5個程度) 3D Model Retrieval, Boosting, Best view selection, Feature saliency