Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Diagram Structure Recognition by Bayesian Conditional Random Fields
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
A new room decoration assistance system based on 3D reconstruction and integrated service
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Learning structured prediction models for image labeling
Learning structured prediction models for image labeling
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper we present a new conditional random field (CRF) model based on Gaussian mixture potentials for indoor image labeling, which is useful in interactive room decoration system. Indoor images which posses many spatial regularities can be efficiently modeled by probabilistic graphical models such as CRF. The potential functions in CRF are usually set empirically and differently for different features depending on applications. We propose a new CRF model based on a general Gaussian mixture potential for different group of features, which has the advantage of labeling accuracy and training simplicity. The new model with belief propagation inference and stochastic gradient descent training is applied to floor region labeling of indoor images in Labelme database. Simulation results and visual effects prove our analysis. Comparing to other CRF models the new approach is more efficient for indoor image labeling tasks.