Food Recognition Using Consensus Vocabularies
Food recognition is an interesting and challenging problem with applications in medical, social and anthropological research areas. The high variability of food images makes the recognition task difficult for current state-of-the-art methods. It has been proved that the exploitation of multiple features to capture complementary aspects of the image contents is useful to improve the discrimination of different food items. In this paper we exploit an image representation based on the consensus among visual vocabularies built on different feature spaces. Starting from a set of visual codebooks, a consensus clustering technique is used to build a consensus vocabulary used to represent food pictures with a Bag-of-Visual-Words paradigm. This new representation is employed together with a SVM for recognition purpose.
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- 1.Battiato, S., Farinella, G.M., Puglisi, G., Ravì, D.: Aligning codebooks for near duplicate image detection. Multimedia Tools and Applications, 1–24 (2013)Google Scholar
- 2.Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: Pfid: Pittsburgh fast-food image dataset. IEEE International Conference on Image Processing, 289–292 (2009)Google Scholar
- 4.Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. IEEE International Conference on Image Processing, 285–288 (2009)Google Scholar
- 7.Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. IEEE International Conference on Multimedia and Expo, 25–30 (2012)Google Scholar
- 8.Matsuda, Y., Yanai, K.: Multiple-food recognition considering co-occurrence employing manifold ranking. In: International Conference on Pattern Recognition, pp. 2017–2020 (2012)Google Scholar
- 10.Saffari, A., Bischof, H.: Clustering in a boosting framework, pp. 75–82. Computer Vision Winter Workshop (2007)Google Scholar
- 11.Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. IEEE Conference on Computer Vision and Pattern Recognition, 1–8 (2008)Google Scholar
- 14.Yang, S., Chen, M., Pomerleau, D., Sukthankar, R.: Food recognition using statistics of pairwise local features. IEEE Conference on Computer Vision and Pattern Recognition, 2249–2256 (2010)Google Scholar
- 15.Farinella, G.M., Moltisanti, M., Battiato, S.: Classifying Food Images Represented as Bag of Textons. IEEE International Conference on Image Processing, 5212–5216 (2014)Google Scholar
- 16.Farinella, G.M., Allegra, D., Stanco, F.: A benchmark dataset to study the representation of food images. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8927, pp. 584–599. Springer, Heidelberg (2015) Google Scholar
- 18.Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 446–461. Springer, Heidelberg (2014) Google Scholar