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

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 super-
vision. 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

  

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

 

Collections: Computer Technologies and Information Sciences