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• W1972508834 abstract "Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction , uncertainty , diversity , density and relevance . We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation." @default.
• W1972508834 created "2016-06-24" @default.
• W1972508834 creator A5024965898 @default.
• W1972508834 creator A5036726873 @default.
• W1972508834 date "2011-02-01" @default.
• W1972508834 modified "2023-10-17" @default.
• W1972508834 title "Active learning in multimedia annotation and retrieval" @default.
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• W1972508834 doi "https://doi.org/10.1145/1899412.1899414" @default.
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