DocumentCode :
2459909
Title :
Non-Parametric Probabilistic Image Segmentation
Author :
Andreetto, Marco ; Zelnik-Manor, Lihi ; Perona, Pietro
Author_Institution :
California Inst. of Technol., Pasadena
fYear :
2007
fDate :
14-21 Oct. 2007
Firstpage :
1
Lastpage :
8
Abstract :
We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks.
Keywords :
Gaussian processes; image segmentation; probability; Gaussians mixture; nonparametric probabilistic image segmentation; probabilistic cluster assignments; probabilistic generative model; Clustering algorithms; Data structures; Gaussian processes; Image generation; Image segmentation; Kernel; Noise shaping; Parametric statistics; Shape; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location :
Rio de Janeiro
ISSN :
1550-5499
Print_ISBN :
978-1-4244-1630-1
Electronic_ISBN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2007.4408968
Filename :
4408968
Link To Document :
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