Paper Info
Title
Active labeling application applied to food-related object recognition
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.
Year
DOI
Venue
2013
10.1145/2506023.2506032
CEA@ACM Multimedia
Keywords
Field
DocType
food-related object recognition,food-related object,expert time labelling,novel method,hierarchical sampling method,hierarchical structure,active forest,automatic method,daily life,life-logging data,better data
Computer vision,Lifelog,Feature vector,Active learning,Computer science,Artificial intelligence,Sampling (statistics),Cluster analysis,Classifier (linguistics),Wearable technology,Machine learning,Cognitive neuroscience of visual object recognition
Conference
Citations 
PageRank 
References 
5
0.45
17
Authors
3
Name
Order
Citations
PageRank
Marc Bolaños1749.24
Maite Garolera2223.97
Petia Radeva31684153.53