Marc Bolaños
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.
- H. Abdi and L. J. Williams. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4):433--459, 2010.Google Scholar
Digital Library
- S. Balakrishnama and A. Ganapathiraju. Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 1998.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- S. Dasgupta. Two faces of active learning. Theoretical computer science, 412(19):1767--1781, 2011. Google ScholarDigital Library
- S. Dasgupta and D. Hsu. Hierarchical sampling for active learning. In Proceedings of the 25th ICML, pages 208--215. ACM, 2008. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- R. O. Duda, P. E. Hart, et al. Pattern classification and scene analysis, volume 3. Wiley New York, 1973.Google Scholar
- 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. Google ScholarDigital Library
- P. H. Gosselin and M. Cord. Active learning methods for interactive image retrieval. Image Processing, IEEE Transactions on, 17(7):1200--1211, 2008. Google ScholarDigital Library
- J. C. Gower and G. Ross. Minimum spanning trees and single linkage cluster analysis. Applied statistics, pages 54--64, 1969.Google Scholar
- J. A. Hartigan. Consistency of single linkage for high-density clusters. Journal of the American Statistical Association, 76(374):388--394, 1981.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- M. Li and I. K. Sethi. Confidence-based active learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(8):1251--1261, 2006. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- T. Seidl and H.-P. Kriegel. Optimal multi-step k-nearest neighbor search. In ACM SIGMOD Record, volume 27, pages 154--165. ACM, 1998. Google ScholarDigital Library
- A. J. Sellen and S. Whittaker. Beyond total capture: a constructive critique of lifelogging. Communications of the ACM, 53(5):70--77, 2010. Google ScholarDigital Library
- B. Settles. Active learning literature survey. University of Wisconsin, Madison, 2010.Google ScholarDigital Library
- 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. Google ScholarDigital Library
- M. Welling. Fisher linear discriminant analysis. Department of Computer Science, University of Toronto, 2005.Google Scholar
Index Terms
- Active labeling application applied to food-related object recognition
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