Recognition of Multiple-Food Images by Detecting Candidate Regions

By Yuji Matsuda, Hajime Hoashi, Keiji Yanai. IEEE International Conference on Multimedia and Expo, 2012

354 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Food Computing (2018, background): “[Matsuda et al. 2012] SIFT, Color HoG, Texture Food recognition [Farinella et al.”
Im2Calories: Towards an Automated Mobile Vision Food Diary (2015, background): “In addition, there are various smaller datasets, such as: PFID [7], which has 61 classes (of fast food) and 1098 images; UNICT-FD889 [14], which has 889 classes and 3853 images; and UECFOOD-100 [24], which has 100 classes (of Japanese food), and 9060 images.”
A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, methodology): “[63] designed a 5-layer CNN model for fruit classification on the UEC-FOOD100 dataset [72] and a self-established fruit dataset.”
Food image recognition using deep convolutional network with pre-training and fine-tuning (2015, methodology): “Therefore, many works have been published so far [1, 2, 3, 4, 5, 6, 7].”