Food-475 Database

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].

Download Food-475DB

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] Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learn-ing. In: Image Processing (ICIP), 2009 16th IEEE International Conference on. pp. 285-288. IEEE (2009)

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 -, pp. -, Elsevier, 2018.

@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},
 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/}}