Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Capture of hair geometry from multiple images
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Detection and Analysis of Hair
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey on Hair Modeling: Styling, Simulation, and Rendering
IEEE Transactions on Visualization and Computer Graphics
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hair photobooth: geometric and photometric acquisition of real hairstyles
ACM SIGGRAPH 2008 papers
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Face Photo-Sketch Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A multi-classifier approach to face image segmentation for travel documents
Expert Systems with Applications: An International Journal
Using structural patches tiling to guide human head-shoulder segmentation
Proceedings of the 20th ACM international conference on Multimedia
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Hair is a very important part of human appearance. Robust and accurate hair segmentation is difficult because of challenging variation of hair color and shape. In this paper, we propose a novel Compositional Exemplar-based Model (CEM) for hair style segmentation. CEM generates an adaptive hair style (a probabilistic mask) for the input image automatically in the manner of Divide-and-Conquer, which can be divided into decomposition stage and composition stage naturally. For the decomposition stage, we learn a strong ranker based on a group of weak similarity functions emphasizing the Semantic Layout similarity (SLS) effectively; in the composition stage, we introduce the Neighbor Label Consistency (NLC) Constraint to reduce the ambiguity between data representation and semantic meaning and then recompose the hair style using alpha-expansion algorithm. Final segmentation result is obtained by Dual-Level Conditional Random Fields. Experiment results on face images from Labeled Faces in the Wild data set show its effectiveness.