Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Automatically Segmenting LifeLog Data into Events
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Beyond total capture: a constructive critique of lifelogging
Communications of the ACM
Image Recognition of 85 Food Categories by Feature Fusion
ISM '10 Proceedings of the 2010 IEEE International Symposium on Multimedia
Theoretical Computer Science
Interactive labeling of WCE images
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
Discovering important people and objects for egocentric video summarization
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Food region segmentation in meal images using touch points
Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities
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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.