A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Expert systems for image processing: knowledge-based composition of image analysis processes
Computer Vision, Graphics, and Image Processing
The nature of statistical learning theory
The nature of statistical learning theory
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
INBS '95 Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems (INBS'95)
A Threshold Selection Technique
IEEE Transactions on Computers
Low Level Image Segmentation: An Expert System
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive thresholding by variational method
IEEE Transactions on Image Processing
Segmentation of bright targets using wavelets and adaptive thresholding
IEEE Transactions on Image Processing
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Automatic clinical image segmentation using pathological modeling, PCA and SVM
Engineering Applications of Artificial Intelligence
Transforming cluster-based segmentation for use in OpenVL by mainstream developers
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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Segmentation of nontrivial images is one of the most important tasks in image processing. It is easy for human being, but extremely difficult for computers. With the purpose of finding optimal segmentation algorithm for every image through learning from human experience, this paper investigates the manual segmentation process and thus presents a performance prediction based algorithm selection model to bridge the knowledge gap between images and segmentation algorithms. Derived from that model, a framework of learning-based algorithm selection system is proposed to automatically segment all images in a large database. A simulation system is designed to select the optimal segmentation algorithm from four candidates for synthetic images. The system is tested on 9000 images by comparing with the manual algorithm selection. The best algorithms are selected for 85% of the cases. If we also regard the second best algorithm as acceptable, more than 97% of images can be properly segmented. The satisfied result demonstrated that this study has provided a promising approach to achieve automated image segmentation.