A Modified All-and-One Classification Algorithm Combined with the Bag-of-Features Model to Address the Food Recognition Task

Kalliopi Dalakleidi, Myriam Sarantea, Konstantina Nikita

Abstract

Dietary intake monitoring can play an important role in reducing the risk of diet related chronic diseases. Automatic systems that support patients to count the nutrient contents, like carbohydrates (CHO), of their meals, can provide valuable tools. In this study, a food recognition system is proposed, which consists of two modules performing feature extraction and classification of food images, respectively. The dataset used consists of 1200 food images split into six categories (bread, meat, potatoes, rice, pasta and vegetables). Speeded Up Robust Features (SURF) along with Color and Local Binary Pattern (LBP) features are extracted from the food images. The Bag-Of-Features (BOF) model is used in order to reduce the features space. A modified version of the All-And-One Support Vector Machine (SVM) is proposed to perform the task of classification and its performance is evaluated against several classifiers that follow the SVM or the K-Nearest Neighbours (KNN) approach. The proposed classification method has achieved the highest levels of accuracy (Acc = 94.2 %) in comparison with all the other classifiers.

References

  1. Anthimopoulos, M., Gianola, L., Scarnato, L., Diem, P. and Mougiakakou, S., 2014. A Food Recognition System for Diabetic Patients Based on an Optimized Bag-ofFeatures Model. Journal of Biomedical and Health Informatics, 18(4), pp. 1261-1271.
  2. Baumberg, A., 2000. Reliable feature matching across widely separated views. In: IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA: IEEE, pp. 774 - 781.
  3. Bay, H., Ess, A., Tuytelaars, T. and Gool, L., 2008. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), pp. 346-359.
  4. Carneiro, G. and Jepson, A., 2003. Multi-scale phase-based local features. In: IEEE Conference on Computer Vision and Pattern Recognition. Madison, Wisconsin, USA: IEEE, pp. 736 - 743.
  5. Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R. and Yang, J., 2009. PFID: Pittsburgh fast-food image dataset. In: IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, pp. 289-292.
  6. Cruz-Roa, A., Caicedo, J. and Gonzalez, F., 2011. Visual pattern mining in histology image collections using bag of features. Artificial Intelligence in Medicine, 52, pp. 91-106.
  7. Dalakleidi, K., Zarkogianni, K., Karamanos, V., Thanopoulou, A. and Nikita, K., 2013. A Hybrid Genetic Algorithm for the Selection of the Critical Features for Risk Prediction of Cardiovascular Complications in Type 2 Diabetes Patients. In: 13th International Conference on BioInformatics and BioEngineering. Chania, Greece: IEEE.
  8. Florack, L., Haar Romeny, B., Koenderink, J. and Viergever, M., 1994. General intensity transformations and differential invariants. Journal of Mathematical Imaging and Vision, 4, pp. 171-187.
  9. Freeman, W. and Adelson, E., 1991. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, pp. 891 - 906.
  10. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F., 2011. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition, 44, pp. 1761-1776.
  11. Gou, J., Xiong, T. and Kuang, Y., 2011. A novel weighted voting for K-Nearest neighbour rule. Computers, 6(5), pp. 833-840.
  12. Joutou, T. and Yanai, K., 2009. A food image recognition system with multiple kernel learning. In: IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, pp. 285-288.
  13. Kong, F. and Tan, J., 2012. DietCam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing, 8(1), pp. 147-163.
  14. Marinakis, Y., Ntounias, G. and Jantzen, J., 2009. Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine, 39, pp. 69-78.
  15. Martin, C., Kaya, S. and Gunturk, B., 2009. Quantification of food intake using food image analysis. In: Engineering in Medicine and Biology Society Annual International Conference of the IEEE. Minneapolis, Minnesota, USA: IEEE, pp. 6869-6872.
  16. Mikolajczyk, K. and Schmid, C., 2003. A performance evaluation of local descriptors, In: IEEE Conference on Computer Vision and Pattern Recognition. Madison, Wisconsin, USA:IEEE, pp. 257 - 263.
  17. Mindru, F., Tuytelaars, T., Van Gool, L. and Moons, T., 2004. Moment invariants for recognition under changing viewpoint and illumination. Computer Vision and Image Understanding, 94, pp. 3-27.
  18. Ojala, T., Pietikäinen, M. and Mäenpää, T., 2002. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), pp. 971-987.
  19. Pedrajas, N. and Boyer, D., 2006. Improving multiclass pattern recognition by the combination of two strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6), pp. 1001-1006.
  20. Peng, X., Wang, L., Wang, X. and Qiao, Y., 2016. Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice. Computer Vision and Image Understanding, pp. 1-17.
  21. Prabhakar, C. and Praveen Kumar, P., 2012. LBP-SURF Descriptor with Color Invariant and Texture Based Features for Underwater Images. In: 8th Indian Conference on Computer Vision, Graphics and Image Processing. Mumbai, India.
  22. Puri, M., Zhu, Z., Yu, Q., Divakaran, A. and Sawhney, H., 2009. Recognition and Volume Estimation of Food Intake using a Mobile Device. In: Workshop on Applications of Computer Vision (WACV). Snowbird, Utah, USA: IEEE, pp. 1-8.
  23. Schaffalitzky, F. and Zisserman, A., 2002. Multi-view matching for unordered image sets, or “How do I organize my holiday snaps?”. In: 7th European Conference on Computer Vision. Copenhagen, Denmark: LNCS, pp. 414 - 431.
  24. Shroff, G., Smailagic, A. and Siewiorek, D., 2008. Wearable context-aware food recognition for calorie monitoring. In: 12th IEEE International Symposium on Wearable Computers. IEEE, pp. 119-120.
  25. Sierra, B., Lazkano, E., Irigoien, I., Jauregi, E. and Mendialdua, I., 2011. K nearest neighbor equality: giving equal chance to all existing classes. Information Sciences, 181(23), pp. 5158-5168.
  26. Wang, R., Ding, K., Yang, J. and Xue, L., 2016. A novel method for image classification based on bag of visual words. Journal of Visual Communication and Image Representation, 40, pp. 24-33.
  27. Yang, S., Chen, M., Pomerleau, D. and Sukthankar, R., 2010. Food recognition using statistics of pairwise local features. In: IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, California, USA:IEEE, pp. 2249-2256.
  28. Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C., Ebert, D. and Delp, E., 2010. The use of mobile devices in aiding dietary assessment and evaluation. IEEE Journal of Selected Topics in Signal Processing, 4(4), pp. 756- 766.
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Paper Citation


in Harvard Style

Dalakleidi K., Sarantea M. and Nikita K. (2017). A Modified All-and-One Classification Algorithm Combined with the Bag-of-Features Model to Address the Food Recognition Task . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 284-290. DOI: 10.5220/0006141302840290


in Bibtex Style

@conference{healthinf17,
author={Kalliopi Dalakleidi and Myriam Sarantea and Konstantina Nikita},
title={A Modified All-and-One Classification Algorithm Combined with the Bag-of-Features Model to Address the Food Recognition Task},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={284-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006141302840290},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - A Modified All-and-One Classification Algorithm Combined with the Bag-of-Features Model to Address the Food Recognition Task
SN - 978-989-758-213-4
AU - Dalakleidi K.
AU - Sarantea M.
AU - Nikita K.
PY - 2017
SP - 284
EP - 290
DO - 10.5220/0006141302840290