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IEEE TRANSACTION ON PATTERN ANALYSIS & MACHINE INTELLIGENCE, VOL. X, NO. X, SEPT XXXX 1 Unsupervised Learning of Categorical Segments in
 

Summary: IEEE TRANSACTION ON PATTERN ANALYSIS & MACHINE INTELLIGENCE, VOL. X, NO. X, SEPT XXXX 1
Unsupervised Learning of Categorical Segments in
Image Collections
Marco Andreetto
, Lihi Zelnik-Manor, Pietro Perona,
Abstract
Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two
simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing
the shape and appearance of each segment, with the popular "bag of visual words" model for recognition. If
applied to a collection of images, our framework can simultaneously discover the segments of each image, and the
correspondence between such segments, without supervision. Such recurring segments may be thought of as the
`parts' of corresponding objects that appear multiple times in the image collection. Thus, the model may be used
for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human
annotation.
Index Terms
Computer Vision, image segmentation, unsupervised object recognition, graphical models, density estimation,
scene analysis
I. INTRODUCTION
Image segmentation and recognition have long been associated in the vision literature. Three views
have been entertained on their relationship: (a) segmentation is a preprocessing step for recognition:

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences