A unified framework for interactive image segmentation via Fisher rules

Interactive image segmentation has been an active research topic in image processing and computer graphics. One of its appealing advantage is the optimization of human feedback and interactions to generate user-desired results. Segmentation results of most previous methods usually depend on the qual...

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Bibliographic Details
Published in:The Visual computer 2019-12, Vol.35 (12), p.1869-1882
Main Authors: Luo, Lingkun, Wang, Xiaofang, Hu, Shiqiang, Hu, Xing, Zhang, Huanlong, Liu, Yaohua, Zhang, James
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computer Graphics
Computer Science
Computer vision
Design
Game theory
Image processing
Image Processing and Computer Vision
Image reconstruction
Image segmentation
Labels
Lagrange multiplier
Methods
Optimization
Original Article
Pattern recognition
Probability
Propagation
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Summary:Interactive image segmentation has been an active research topic in image processing and computer graphics. One of its appealing advantage is the optimization of human feedback and interactions to generate user-desired results. Segmentation results of most previous methods usually depend on the quality and quantity of the user input. In this paper, we propose an algorithm to solve one important challenge arising from inputs with bad or limited quality. Our work is notably different from previous methods. First, the weakly interactive image segmentation is formulated and deduced in theory, then we propose to reconstruct enough samples via sparse reconstruction to enhance the robustness to weakly interactive labels. More importantly, we leverage interactive labels to extract a latent subspace which jointly optimizes multiclass classification and binary classification based on fisher rules. Numerous experiments are conducted on MSRC (Ning et al. in Interact Imaging Vis Pattern Recognit 43(2):445–456, 2010 ) and KIM (Kim et al. in: 2010 IEEE computer society conference on computer vision and pattern recognition, 2010 ) database. The results demonstrate effectiveness and efficiency of our method.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-018-1580-0