Classification of Food Images through Interactive Image Segmentation

Food item segmentation in an image is a kind of fine-grained segmentation task, which is comparatively difficult than conventional image segmentation because intra-class variance is high and inter-class variance is low. So, an interactive food item segmentation algorithm using Random Forest is proposed in this work. The first step of the proposed algorithm is interactive food image segmentation, where food parts are extracted based on user inputs. It is observed that some of the segmented food parts may have some holes due to improper distribution of light. So, Boundary Detection & Filling and Gappy Principal Component Analysis methods are applied to restore the missing information in the second step. Local Binary Pattern and Non Redundant Local Binary Pattern are used for extracting features from the restored food parts, which are fed into support vector machine classifier for differentiating one food image from others. All the experiments have been performed on Food 101 database. A comparative study has also been done based on the three existing methods. The obtained results demonstrate that the proposed method outperforms the existing methods.