Unsupervised learning of categorical segments in image collections.
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.
Google Los Angeles (US-LAX-BIN), 340 Main Street, Venice, CA 90291, USA. marco@vision.caltech.edu
Marco
M
Andreetto
Lihi
L
Zelnik-Manor
Pietro
P
Perona
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016208:Databases, Factual; D005145:Face; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated / Q000379:methods*; D014197:Trees