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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/12204

タイトル: Sequential Attention Planning for Large-scale Environment Classification
著者: Lee, Hosun
Jeong, Sungmoon
Chong, Nak Young
キーワード: Attention path planning
Sequential feature extraction
Environment clasification
Submodular optimization
Greedy algorithm
発行日: 2014-06-01
誌名: ICRA 2014 Workshop: Robots in Homes and Industry: Where to Look Firrst?
開始ページ: 1
終了ページ: 6
抄録: In this paper, a sequential attention planning algorithm is proposed and applied to wide-uncertain environment classification with small field-of-view cameras. Attention planning is formulated as the sequential feature selection problem that greedily finds a sequence of attentions to obtain more informative observations, yielding faster training and higher accuracies. However, greedy algorithm cannot always guarantee the optimal solution. In order to find the near-optimal solution for attention planning, adaptive submodular optimization is considered under certain assumptions, where the objective function for the internal belief is adaptive submodular and adaptive monotone. First, the amount of information of individual attention area is modeled as dissimilarity variance among the environment data set, respectively. With this model, the information gain function is defined as a function of variance reduction that has been shown to be submodular and monotone in many cases. Furthermore, adapting to increasing numbers of observations, each information gain for attention areas is iteratively updated by discarding the non-informative prior knowledge about current environment using a cascade of nearest neighbor classifiers, enabling to maximize the expected information gain. The effectiveness of the proposed algorithm is verified through experiments that can significantly enhance the uncertain environment classification accuracy, with reduced number of limited field-of-view observations.
Rights: Sequential Attention Planning for Large-scale Environment Classification, Hosun Lee, Sungmoon Jeong, and Nak Young Chong, ICRA 2014 Workshop: Robots in Homes and Industry: Where to Look Firrst?, 2014, pp.1-6.
URI: http://hdl.handle.net/10119/12204
資料タイプ: author
出現コレクション:b11-1. 会議発表論文・発表資料 (Conference Papers)


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