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Unsupervised Learning of Categorical Segments in Image Collections Marco Andreetto
 

Summary: Unsupervised Learning of Categorical Segments in Image Collections
Marco Andreetto
Lihi Zelnik-Manor
Pietro Perona

Dept. of Electrical Engineering
Dept. of Electrical Engineering
California Institute of Technology Technion - Israel Institute of Technology
Pasadena, CA 91125, United States Haifa, 32000, Israel
Abstract
Which one comes first: segmentation or recognition? We
propose a probabilistic framework for carrying out the
two simultaneously. The framework combines an LDA
`bag of visual words' model for recognition, and a hybrid
parametric-nonparametric model for segmentation. If ap-
plied to a collection of images, our framework can simulta-
neously discover the segments of each image, and the corre-
spondence between such segments. Such segments may be
thought of as the `parts' of corresponding objects that ap-
pear in the image collection. Thus, the model may be used

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology
Barr, Al - Computer Science Department, California Institute of Technology
Zelnik-Manor, Lihi - Zelnik-Manor, Lihi - Department of Electrical Engineering, Technion, Israel Institute of Technology

 

Collections: Computer Technologies and Information Sciences; Engineering