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dc.contributor.authorZhu, F.
dc.contributor.authorBosch, M.
dc.contributor.authorKhanna, N.
dc.contributor.authorBoushey, Carol
dc.contributor.authorDelp, E.
dc.identifier.citationZhu, F. and Bosch, M. and Khanna, N. and Boushey, C. and Delp, E. 2015. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE Journal of Biomedical and Health Informatics. 19 (1): pp. 377-388.

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

dc.publisherInstitute of Electrical and Electronics Engineers
dc.titleMultiple hypotheses image segmentation and classification with application to dietary assessment
dc.typeJournal Article
dcterms.source.titleIEEE Journal of Biomedical and Health Informatics
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available

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