Food-475 database is one of the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases. The Food-475 Dataset is a refinement of the Food524DB [1] composite database obtained by sematically merging equivalent food classes of the four food datasets: UECFOOD256 [2], VIREO [3], Food-101 [4], and Food-50 [5].
Examples of semantically related food classes.
Download Food-475DB
To download the database information, follow this link:
If you use this database, please cite the following paper:
@article{ciocca2018cnn-based,
author = {Ciocca, Gianluigi and Napoletano, Paolo and Schettini, Raimondo},
year = {2018},
pages = {-},
title = {CNN-based Features for Retrieval and Classification of Food Images},
volume = {-},
publisher = {Elsevier},
journal = {Computer Vision and Image Understanding},
doi = {10.1016/j.cviu.2018.09.001}
}
[1] Gianluigi Ciocca, Paolo Napoletano, Raimondo Schettini: Learning CNN-based Features for Retrieval of Food Images. In New Trends in Image Analysis and Processing — ICIAP 2017: ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected Papers, Cham, pp. 426-434, Springer International Publishing, 2017.
[2] Kawano, Y., Yanai, K.: Automatic expansion of a food image dataset leverag-ing existing categories with domain adaptation. In: Proc. of ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (2014)
[3] Chen, J., Ngo, C.W.: Deep-based ingredient recognition for cooking recipe retrieval.
In: Proc. of the 2016 ACM on Multimedia Conference. pp. 32-41. ACM (2016)
[4] Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 mining discriminative compo-nents with random forests. In: Computer Vision ECCV 2014, pp. 446-461 (2014)
[5] Chen, M. Y., Yang, Y. H., Ho, C. J., Wang, S. H., Liu, S. M., Chang, E., Ouhyoung, M. (2012, November). Automatic chinese food identification and quantity estimation. In SIGGRAPH Asia 2012 Technical Briefs (p. 29). ACM.
Publications
1.
CNN-based Features for Retrieval and Classification of Food Images (Gianluigi Ciocca, Paolo Napoletano, Raimondo Schettini)
In Computer Vision and Image Understanding, volume 176--177, pp. 70-77, Elsevier, 2018.
@article{ciocca2018cnn-based, author = {Ciocca, Gianluigi and Napoletano, Paolo and Schettini, Raimondo}, year = {2018}, pages = {70-77}, title = {CNN-based Features for Retrieval and Classification of Food Images}, volume = {176--177}, publisher = {Elsevier}, journal = {Computer Vision and Image Understanding}, pdf = {/download/CVIU-food.pdf}, doi = {10.1016/j.cviu.2018.09.001}, projectref = {http://www.ivl.disco.unimib.it/activities/food475db/}}
2.
Learning CNN-based Features for Retrieval of Food Images (Gianluigi Ciocca, Paolo Napoletano, Raimondo Schettini)
In New Trends in Image Analysis and Processing -- ICIAP 2017: ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected Papers, Cham, pp. 426-434, Springer International Publishing, 2017.
@inproceedings{ciocca2017Learning-CNN, author = {Ciocca, Gianluigi and Napoletano, Paolo and Schettini, Raimondo}, editor = {Battiato, Sebastiano and Farinella, Giovanni Maria and Leo, Marco and Gallo, Giovanni}, year = {2017}, pages = {426-434}, title = {Learning CNN-based Features for Retrieval of Food Images}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-70742-6}, booktitle = {New Trends in Image Analysis and Processing -- ICIAP 2017: ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected Papers}, pdf = {/download/Ciocca2017learning-cnn.pdf}, doi = {10.1007/978-3-319-70742-6_41}, projectref = {http://www.ivl.disco.unimib.it/activities/food524db/}}
