A semisupervised segmentation model for collections of images

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Yan Nei LawAndy M Yip

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.

References

Apr 16, 2004·IEEE Transactions on Medical Imaging·Joes StaalBram van Ginneken
Sep 21, 2004·IEEE Transactions on Pattern Analysis and Machine Intelligence·Stella X Yu, Jianbo Shi
Mar 4, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yuri Boykov, Vladimir Kolmogorov
Sep 14, 2006·IEEE Transactions on Medical Imaging·João V B SoaresMichael J Cree
Apr 5, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Alexis Protiere, Guillermo Sapiro
Nov 3, 2009·BMC Bioinformatics·Elisa Drelie GelascaBs Manjunath

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Citations

Jul 23, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Le WangNanning Zheng
May 27, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jamshid SouratiDana H Brooks
Mar 2, 2017·Medical & Biological Engineering & Computing·Nuh Hatipoglu, Gokhan Bilgin
Mar 16, 2019·Scientific Reports·Mahmoud AbdolhoseiniSarah J Johnson

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