Paper Title:
Shared Parts Latent Topic Model for Image Classification
  Abstract

This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.

  Info
Periodical
Advanced Materials Research (Volumes 271-273)
Edited by
Junqiao Xiong
Pages
1257-1262
DOI
10.4028/www.scientific.net/AMR.271-273.1257
Citation
B. Yang, "Shared Parts Latent Topic Model for Image Classification", Advanced Materials Research, Vols. 271-273, pp. 1257-1262, 2011
Online since
July 2011
Authors
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Price
$32.00
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