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Title: A semisupervised segmentation model for collections of images
Authors: Law, Y.N.
Lee, H.K.
Ng, M.K.
Yip, A.M. 
Keywords: Biological image segmentation
Image segmentation
Microscopy images
Multiple images
Issue Date: Jun-2012
Citation: Law, Y.N., Lee, H.K., Ng, M.K., Yip, A.M. (2012-06). A semisupervised segmentation model for collections of images. IEEE Transactions on Image Processing 21 (6) : 2955-2968. ScholarBank@NUS Repository.
Abstract: In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model. Second, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. We will show the mathematical properties of the model such as existence and uniqueness of solution and establish a maximum/minimum principle for the solution of the model. Extensive experiments on various collections of biological images suggest that the proposed model is effective for segmentation and is computationally efficient. © 2012 IEEE.
Source Title: IEEE Transactions on Image Processing
ISSN: 10577149
DOI: 10.1109/TIP.2012.2187670
Appears in Collections:Staff Publications

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