Paper Info
Title
A Generative Model for Parts-based Object Segmentation.
Abstract
The Shape Boltzmann Machine (SBM) has recently been introduced as a state-of-the-art model of foreground/background object shape. We extend the SBM to account for the foreground object's parts. Our model, the Multinomial SBM (MSBM), can capture both local and global statistics of part shapes accurately. We combine the MSBM with an appearance model to form a fully generative model of images of objects. Parts-based image segmentations are obtained simply by performing probabilistic inference in the model. We apply the model to two challenging datasets which exhibit significant shape and appearance variability, and find that it obtains results that are comparable to the state-of-the-art.
Year
Venue
Keywords
2012
NIPS
machine vision
Field
DocType
Citations 
Probabilistic inference,Boltzmann machine,Pattern recognition,Segmentation,Computer science,Multinomial distribution,Active appearance model,Artificial intelligence,Machine learning,Global statistics,Generative model
Conference
16
PageRank 
References 
Authors
0.78
18
2
Name
Order
Citations
PageRank
S. M. Ali Eslami1161.46
Christopher K. I. Williams26807631.16