The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
76
Dalam beberapa tahun kebelakangan ini, peningkatan dalam pemakanan sihat telah membawa kepada pelbagai aplikasi pengurusan makanan yang mempunyai fungsi pengecaman imej untuk merakam makanan harian secara automatik. Walau bagaimanapun, kebanyakan fungsi pengecaman imej dalam aplikasi sedia ada tidak berguna secara langsung untuk foto makanan berbilang hidangan dan tidak boleh menganggar kalori makanan secara automatik. Sementara itu, metodologi pada pengecaman imej telah berkembang pesat kerana kemunculan Rangkaian Neural Konvolusi (CNN). CNN telah meningkatkan ketepatan pelbagai jenis tugas pengecaman imej seperti pengelasan dan pengesanan objek. Oleh itu, kami mencadangkan anggaran kalori makanan berasaskan CNN untuk foto makanan berbilang hidangan. Kaedah kami menganggarkan lokasi hidangan dan kalori makanan secara serentak dengan pembelajaran pelbagai tugas pengesanan hidangan makanan dan anggaran kalori makanan dengan satu CNN. Ia dijangka mencapai kelajuan tinggi dan saiz rangkaian kecil dengan anggaran serentak dalam satu rangkaian. Oleh kerana pada masa ini tiada set data foto makanan berbilang hidangan beranotasi dengan kedua-dua kotak sempadan dan kalori makanan, dalam kerja ini kami menggunakan dua jenis set data secara bergilir-gilir untuk melatih satu CNN. Untuk dua jenis set data, kami menggunakan foto makanan berbilang hidangan beranotasi dengan kotak sempadan dan foto makanan hidangan tunggal dengan kalori makanan. Keputusan kami menunjukkan bahawa kaedah pelbagai tugas kami mencapai ketepatan yang lebih tinggi, kelajuan yang lebih tinggi dan saiz rangkaian yang lebih kecil daripada model berurutan pengesanan makanan dan anggaran kalori makanan.
Takumi EGE
The University of Electro-Communications
Keiji YANAI
The University of Electro-Communications
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Salinan
Takumi EGE, Keiji YANAI, "Simultaneous Estimation of Dish Locations and Calories with Multi-Task Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1240-1246, July 2019, doi: 10.1587/transinf.2018CEP0004.
Abstract: In recent years, a rise in healthy eating has led to various food management applications which have image recognition function to record everyday meals automatically. However, most of the image recognition functions in the existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network (CNN). CNN has improved accuracies of various kinds of image recognition tasks such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates dish locations and food calories simultaneously by multi-task learning of food dish detection and food calorie estimation with a single CNN. It is expected to achieve high speed and small network size by simultaneous estimation in a single network. Because currently there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, in this work we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos annotated with bounding boxes and single-dish food photos with food calories. Our results showed that our multi-task method achieved higher accuracy, higher speed and smaller network size than a sequential model of food detection and food calorie estimation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018CEP0004/_p
Salinan
@ARTICLE{e102-d_7_1240,
author={Takumi EGE, Keiji YANAI, },
journal={IEICE TRANSACTIONS on Information},
title={Simultaneous Estimation of Dish Locations and Calories with Multi-Task Learning},
year={2019},
volume={E102-D},
number={7},
pages={1240-1246},
abstract={In recent years, a rise in healthy eating has led to various food management applications which have image recognition function to record everyday meals automatically. However, most of the image recognition functions in the existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network (CNN). CNN has improved accuracies of various kinds of image recognition tasks such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates dish locations and food calories simultaneously by multi-task learning of food dish detection and food calorie estimation with a single CNN. It is expected to achieve high speed and small network size by simultaneous estimation in a single network. Because currently there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, in this work we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos annotated with bounding boxes and single-dish food photos with food calories. Our results showed that our multi-task method achieved higher accuracy, higher speed and smaller network size than a sequential model of food detection and food calorie estimation.},
keywords={},
doi={10.1587/transinf.2018CEP0004},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Simultaneous Estimation of Dish Locations and Calories with Multi-Task Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1240
EP - 1246
AU - Takumi EGE
AU - Keiji YANAI
PY - 2019
DO - 10.1587/transinf.2018CEP0004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - In recent years, a rise in healthy eating has led to various food management applications which have image recognition function to record everyday meals automatically. However, most of the image recognition functions in the existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network (CNN). CNN has improved accuracies of various kinds of image recognition tasks such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates dish locations and food calories simultaneously by multi-task learning of food dish detection and food calorie estimation with a single CNN. It is expected to achieve high speed and small network size by simultaneous estimation in a single network. Because currently there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, in this work we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos annotated with bounding boxes and single-dish food photos with food calories. Our results showed that our multi-task method achieved higher accuracy, higher speed and smaller network size than a sequential model of food detection and food calorie estimation.
ER -