PT Unknown
AU Michal Drozdzal
   Santiago Segui
   Carolina Malagelada
   Fernando Azpiroz
   Jordi Vitria
   Petia Radeva
TI Interactive Labeling of WCE Images
BT 5th Iberian Conference on Pattern Recognition and Image Analysis
PY 2011
BP 143
EP 150
VL 6669
AB A high quality labeled training set is necessary for any supervised machine learning algorithm. Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. The problem that occurs is the data diversity, in the space of the features, from different WCE studies. That means that when new data arrives it is highly probable that it will not be represented in the training set, thus getting a high probability of performing an error when applying machine learning schemes. In this paper an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. It is shown that the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks
ER