DocumentCode :
1757586
Title :
Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment
Author :
Fengqing Zhu ; Bosch, Marc ; Khanna, Neha ; Boushey, Carol J. ; Delp, Edward J.
Author_Institution :
Huawei Technol., Santa Clara, CA, USA
Volume :
19
Issue :
1
fYear :
2015
fDate :
Jan. 2015
Firstpage :
377
Lastpage :
388
Abstract :
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.
Keywords :
biomedical optical imaging; decision making; feature extraction; feature selection; image classification; image matching; image segmentation; medical image processing; object recognition; vectors; automatic food identification; automatic food location; class decision combination; classifier confidence score assignment; classifier feedback; controlled eating event; decision rule; dietary assessment application; feature vector classification; food image segmention accuracy; global feature; image capture; image segmentation accuracy assessment; local feature; multichannel feature classification system; multiple feature space; multiple hypothesis image classification; multiple hypothesis image segmentation; multiple image segmentation generation; natural eating event; perceptually similar object class; segmented object partitioning; segmented region classification; stable segmentation selection; Entropy; Image color analysis; Image edge detection; Image segmentation; Informatics; Support vector machines; Vectors; Dietary assessment; image analysis; image features; image segmentation; object recognition;
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2014.2304925
Filename :
6733271
Link To Document :
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