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dc.contributor.authorTekin, C.en_US
dc.contributor.authorSchaar, Mihaela van deren_US
dc.date.accessioned2019-02-13T08:07:10Z
dc.date.available2019-02-13T08:07:10Z
dc.date.issued2015-11en_US
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/11693/49385
dc.description.abstractWe 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.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Image Processingen_US
dc.relation.isversionofhttps://doi.org/10.1109/TIP.2015.2446936en_US
dc.subjectImage stream miningen_US
dc.subjectActive learningen_US
dc.subjectOnline classificationen_US
dc.subjectOnline learningen_US
dc.subjectContextual expertsen_US
dc.subjectBreast cancer diagnosisen_US
dc.titleActive learning in context-driven stream mining with an application to ımage miningen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage3666en_US
dc.citation.epage3679en_US
dc.citation.volumeNumber24en_US
dc.citation.issueNumber11en_US
dc.identifier.doi10.1109/TIP.2015.2446936en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.identifier.eissn1941-0042


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