research-article

Active labeling application applied to food-related object recognition

Authors:

Maite Garolera,

Petia RadevaAuthors Info & Claims

CEA '13: Proceedings of the 5th international workshop on Multimedia for cooking & eating activities

Pages 45 - 50

https://doi.org/10.1145/2506023.2506032

Published: 21 October 2013 Publication History

Abstract

Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we pro- pose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.

References

[1]

H. Abdi and L. J. Williams. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4):433--459, 2010.

Digital Library

[2]

S. Balakrishnama and A. Ganapathiraju. Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 1998.

[3]

X. Baró, S. Escalera, J. Vitrià, O. Pujol, and P. Radeva. Traffic sign recognition using evolutionary adaboost detection and forest-ecoc classification. Intelligent Transportation Systems, IEEE Transactions on, 10(1):113--126, 2009.

Digital Library

[4]

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. PAMI, IEEE Trans. on, 19(7):711--720, 1997.

Digital Library

[5]

S. Dasgupta. Two faces of active learning. Theoretical computer science, 412(19):1767--1781, 2011.

Digital Library

[6]

S. Dasgupta and D. Hsu. Hierarchical sampling for active learning. In Proceedings of the 25th ICML, pages 208--215. ACM, 2008.

Digital Library

[7]

A. R. Doherty and A. F. Smeaton. Automatically segmenting lifelog data into events. In Image Analysis for Multimedia Interactive Services, WIAMIS'08, pages 20--23. IEEE, 2008.

Digital Library

[8]

M. Drozdzal, S. Seguí, C. Malagelada, F. Azpiroz, J. Vitrià, and P. Radeva. Interactive labeling of wce images. In Pattern Recognition and Image Analysis, pages 143--150. Springer, 2011.

Digital Library

[9]

R. O. Duda, P. E. Hart, et al. Pattern classification and scene analysis, volume 3. Wiley New York, 1973.

[10]

Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1):119--139, 1997.

Digital Library

[11]

P. H. Gosselin and M. Cord. Active learning methods for interactive image retrieval. Image Processing, IEEE Transactions on, 17(7):1200--1211, 2008.

Digital Library

[12]

J. C. Gower and G. Ross. Minimum spanning trees and single linkage cluster analysis. Applied statistics, pages 54--64, 1969.

[13]

J. A. Hartigan. Consistency of single linkage for high-density clusters. Journal of the American Statistical Association, 76(374):388--394, 1981.

[14]

H. Hoashi, T. Joutou, and K. Yanai. Image recognition of 85 food categories by feature fusion. In Multimedia (ISM), 2010 IEEE International Symposium on, pages 296--301. IEEE, 2010.

Digital Library

[15]

S. Hodges, L. Williams, E. Berry, S. Izadi, J. Srinivasan, A. Butler, G. Smyth, N. Kapur, and K. Wood. Sensecam: A retrospective memory aid. In UbiComp 2006: Ubiquitous Computing, pages 177--193. Springer, 2006.

Digital Library

[16]

Y. J. Lee, J. Ghosh, and K. Grauman. Discovering important people and objects for egocentric video summarization. In IEEE Conference on CVPR, pages 1346--1353. IEEE, 2012.

Digital Library

[17]

M. Li and I. K. Sethi. Confidence-based active learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(8):1251--1261, 2006.

Digital Library

[18]

S. W. Lichtman, K. Pisarska, E. R. Berman, M. Pestone, H. Dowling, E. Offenbacher, H. Weisel, S. Heshka, D. E. Matthews, and S. B. Heymsfield. Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. New England Journal of Medicine, 327(27):1893--1898, 1992.

[19]

J. MacQueen et al. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, page 14. California, USA, 1967.

[20]

C. Morikawa, H. Sugiyama, and K. Aizawa. Food region segmentation in meal images using touch points. In Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities, pages 7--12. ACM, 2012.

Digital Library

[21]

P. Radeva, M. Drozdzal, S. Segui, L. Igual, C. Malagelada, F. Azpiroz, and J. Vitria. Active labeling: Application to wireless endoscopy analysis. In International Conference on HPCS'2012, pages 174--181. IEEE, 2012.

[22]

T. Seidl and H.-P. Kriegel. Optimal multi-step k-nearest neighbor search. In ACM SIGMOD Record, volume 27, pages 154--165. ACM, 1998.

Digital Library

[23]

A. J. Sellen and S. Whittaker. Beyond total capture: a constructive critique of lifelogging. Communications of the ACM, 53(5):70--77, 2010.

Digital Library

[24]

B. Settles. Active learning literature survey. University of Wisconsin, Madison, 2010.

Digital Library

[25]

S. Tong and D. Koller. Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research, 2:45--66, 2002.

Digital Library

[26]

M. Welling. Fisher linear discriminant analysis. Department of Computer Science, University of Toronto, 2005.

Cited By

Garcia-Gancedo LBate A(2022)Digital biomarkers for post-licensure safety monitoringDrug Discovery Today10.1016/j.drudis.2022.10335427:11(103354)Online publication date: Nov-2022
https://doi.org/10.1016/j.drudis.2022.103354
Bate AHobbiger S(2020)Artificial Intelligence, Real-World Automation and the Safety of MedicinesDrug Safety10.1007/s40264-020-01001-7Online publication date: 7-Oct-2020
https://doi.org/10.1007/s40264-020-01001-7
Niewiadomski RCeccaldi EHuisman GVolpe GMancini M(2019)Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCIFrontiers in Robotics and AI10.3389/frobt.2019.001196Online publication date: 5-Dec-2019
https://doi.org/10.3389/frobt.2019.00119
Show More Cited By

Index Terms

Active labeling application applied to food-related object recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

Recommendations

Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Computer Vision – ECCV 2020
Abstract
Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, accessible unlabeled data are not ...
Object recognition via local patch labelling
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning

In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of this problem is to be able to recognize any member of a category of objects in ...
Automatic online labeling images via co-active-learning
ICIMCS '09: Proceedings of the First International Conference on Internet Multimedia Computing and Service

The well-built dataset is a pre-requisite for computer vision research. However, the process of collecting and labeling the images is laborious and monotonous. In this paper, we aim to automatic labeling and collecting the images for the visual object ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CEA '13: Proceedings of the 5th international workshop on Multimedia for cooking & eating activities

October 2013

90 pages

ISBN:9781450323925

DOI:10.1145/2506023

General Chair:
Kiyoharu Aizawa
The University of Tokyo, Japan
,
Program Chair:
Takuya Funatomi
Kyoto University, Japan
,
Publications Chair:
Yoko Yamakata
Kyoto University, Japan

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '13

Sponsor:

SIGMM

MM '13: ACM Multimedia Conference

October 21, 2013

Barcelona, Spain

Acceptance Rates

CEA '13 Paper Acceptance Rate 13 of 21 submissions, 62%;

Overall Acceptance Rate 20 of 33 submissions, 61%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
220
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Garcia-Gancedo LBate A(2022)Digital biomarkers for post-licensure safety monitoringDrug Discovery Today10.1016/j.drudis.2022.10335427:11(103354)Online publication date: Nov-2022
https://doi.org/10.1016/j.drudis.2022.103354
Bate AHobbiger S(2020)Artificial Intelligence, Real-World Automation and the Safety of MedicinesDrug Safety10.1007/s40264-020-01001-7Online publication date: 7-Oct-2020
https://doi.org/10.1007/s40264-020-01001-7
Niewiadomski RCeccaldi EHuisman GVolpe GMancini M(2019)Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCIFrontiers in Robotics and AI10.3389/frobt.2019.001196Online publication date: 5-Dec-2019
https://doi.org/10.3389/frobt.2019.00119
Gatto NSobel RGeier JMo JBate AReynolds R(2019)The Role of Pharmacoepidemiology in IndustryPharmacoepidemiology10.1002/9781119413431.ch7(98-125)Online publication date: 18-Oct-2019
https://doi.org/10.1002/9781119413431.ch7
Bate AReynolds RCaubel P(2017)The hope, hype and reality of Big Data for pharmacovigilanceTherapeutic Advances in Drug Safety10.1177/20420986177364229:1(5-11)Online publication date: 31-Oct-2017
https://doi.org/10.1177/2042098617736422
Reyes CMohedano EMcGuinness KO'Connor NGiro-i-Nieto XGurrin CGiro-i-Nieto XRadeva PJoho HJohansen HSingh VDimiccoli M(2016)Where is my Phone?Proceedings of the first Workshop on Lifelogging Tools and Applications10.1145/2983576.2983582(55-62)Online publication date: 16-Oct-2016
https://dl.acm.org/doi/10.1145/2983576.2983582
Bolaños MGarolera MRadeva P(2015)Object Discovery Using CNN Features in Egocentric VideosPattern Recognition and Image Analysis10.1007/978-3-319-19390-8_8(67-74)Online publication date: 9-Jun-2015
https://doi.org/10.1007/978-3-319-19390-8_8
Bolaños MGarolera MRadeva P(2014)Video Segmentation of Life-Logging VideosArticulated Motion and Deformable Objects10.1007/978-3-319-08849-5_1(1-9)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08849-5_1

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents