Confidence based active learning for whole object image segmentation

Authors:
Aiyesha Ma;Nilesh Patel;Mingkun Li;Ishwar K. Sethi
Affiliations:
Department of Computer Science and Engineering, Oakland University, Rochester, Michigan;University of Michigan–Dearborn, Dearborn, Michigan;DOE Joint Genome Institute, Walnut Creek, California;Department of Computer Science and Engineering, Oakland University, Rochester, Michigan
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

Citing 5
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
"GrabCut": interactive foreground extraction using iterated graph cuts

ACM SIGGRAPH 2004 Papers
SVM-Based Classifier Design with Controlled Confidence

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01

Active learning for interactive 3d image segmentation

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In selective object segmentation, the goal is to extract the entire object of interest without regards to homogeneous regions or object shape. In this paper we present the selective image segmentation problem as a classification problem, and use active learning to train an image feature classifier to identify the object of interest. Since our formulation of this segmentation problem uses human interaction, active learning is used for training to minimize the training effort needed to segment the object. Results using several images with known ground truth are presented to show the efficacy of our approach for segmenting the object of interest in still images. The approach has potential applications in medical image segmentation and content-based image retrieval among others.