DietCam: Multiview Food Recognition Using a Multikernel SVM

Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.

[1]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[2]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  P. B. Ryan,et al.  Analysis of dietary intake of selected metals in the NHEXAS-Maryland investigation. , 2001, Environmental health perspectives.

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[5]  E. Finkelstein,et al.  National medical spending attributable to overweight and obesity: how much, and who's paying? , 2003, Health affairs.

[6]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[7]  Manik Varma,et al.  Learning The Discriminative Power-Invariance Trade-Off , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Roberto Cipolla,et al.  Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Herman Adlercreutz,et al.  Plasma alkylresorcinol metabolites as potential biomarkers of whole-grain wheat and rye cereal fibre intakes in women , 2009, British Journal of Nutrition.

[10]  Lei Yang,et al.  PFID: Pittsburgh fast-food image dataset , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Wen Wu,et al.  Fast food recognition from videos of eating for calorie estimation , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[12]  Corby K. Martin,et al.  Quantification of food intake using food image analysis , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Mei Chen,et al.  Food recognition using statistics of pairwise local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Agneta Yngve,et al.  The epidemic of obesity publications, award to legend and more , 2010, Public Health Nutrition.

[17]  Annette Stafleu,et al.  Validation of an FFQ and options for data processing using the doubly labelled water method in children , 2010, Public Health Nutrition.

[18]  Tom Greene,et al.  Validation of a dietary history questionnaire for American Indian and Alaska Native people. , 2010, Ethnicity & disease.

[19]  David S. Ebert,et al.  The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[20]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[21]  James W Hardin,et al.  Relation of Children's Dietary reporting accuracy to cognitive ability. , 2011, American journal of epidemiology.

[22]  Jindong Tan,et al.  DietCam: Regular Shape Food Recognition with a Camera Phone , 2011, 2011 International Conference on Body Sensor Networks.

[23]  J Wylie-Rosett,et al.  A lifestyle assessment and intervention tool for pediatric weight management: the HABITS questionnaire. , 2011, Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.

[24]  Marios Anthimopoulos,et al.  A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model , 2014, IEEE Journal of Biomedical and Health Informatics.

[25]  Jindong Tan,et al.  Recognition of Car Makes and Models From a Single Traffic-Camera Image , 2015, IEEE Transactions on Intelligent Transportation Systems.