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      • Department of Electrical and Electronics Engineering
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      Active learning in context-driven stream mining with an application to ımage mining

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      Author
      Tekin, C.
      Schaar, Mihaela van der
      Date
      2015-11
      Source Title
      IEEE Transactions on Image Processing
      Print ISSN
      1057-7149
      Electronic ISSN
      1941-0042
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      24
      Issue
      11
      Pages
      3666 - 3679
      Language
      English
      Type
      Article
      Item Usage Stats
      115
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      107
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      Abstract
      We propose an image stream mining method in which images arrive with contexts (metadata) and need to be processed in real time by the image mining system (IMS), which needs to make predictions and derive actionable intelligence from these streams. After extracting the features of the image by preprocessing, IMS determines online the classifier to use on the extracted features to make a prediction using the context of the image. A key challenge associated with stream mining is that the prediction accuracy of the classifiers is unknown, since the image source is unknown; thus, these accuracies need to be learned online. Another key challenge of stream mining is that learning can only be done by observing the true label, but this is costly to obtain. To address these challenges, we model the image stream mining problem as an active, online contextual experts problem, where the context of the image is used to guide the classifier selection decision. We develop an active learning algorithm and show that it achieves regret sublinear in the number of images that have been observed so far. To further illustrate and assess the performance of our proposed methods, we apply them to diagnose breast cancer from the images of cellular samples obtained from the fine needle aspirate of breast mass. Our findings show that very high diagnosis accuracy can be achieved by actively obtaining only a small fraction of true labels through surgical biopsies. Other applications include video surveillance and video traffic monitoring.
      Keywords
      Image stream mining
      Active learning
      Online classification
      Online learning
      Contextual experts
      Breast cancer diagnosis
      Permalink
      http://hdl.handle.net/11693/49385
      Published Version (Please cite this version)
      https://doi.org/10.1109/TIP.2015.2446936
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      • Department of Electrical and Electronics Engineering 3650
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