Multilevel Segmentation for Food Classification in Dietary Assessment.

Given a dataset of images, we seek to automatically identify and locate perceptually similar objects. We combine two ideas 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 each image and then learning the object class by combining different segmentations to generate optimal segmentation. We demonstrate that the proposed method can be used as part of a new dietary assessment tool to automatically identify and locate the foods in a variety of food images captured during different user studies.