保有知識の確信度に基づく対話型映像認識理解システムの質問生成戦略(テーマセッション,コンピュータビジョンとパターン認識のための機械学習と最適化,一般)

概要

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This report proposes a method for action planning in a system of interactive visual scene understanding through the use of system knowledge and its confidence. The knowledge confidence is defined as the combination of the following two properties on the latent space of a topic model connecting image features and text labels: 1) Similarity between an input sample and training samples on the latent space, and 2) the overall associability between each text label as determined by the content of the training samples. We evaluate the proposed method in the context of annotation accuracy and effort for providing answers from users. The experimental results with PASCAL V0C2008 dataset indicate that our proposed method achieved comparable or better annotation accuracy with less effort compared with strategies of 1) always asking the name of objects and 2) generating random questions.
2010-08-29