Homotopy-based semi-supervised hidden Markov tree for texture analysis

Published

Journal Article

A semi-supervised hidden Markov tree (HMT) model is developed for texture analysis, incorporating both labeled and unlabeled data for training; the optimal balance between labeled and unlabeled data is estimated via the homotopy method. In traditional EM-based semi-supervised modeling, this balance is dictated by the relative size of labeled and unlabeled data, often leading to poor performance. Semi-supervised modeling may be viewed as a source allocation problem between labeled and unlabeled data, controlled by a parameter λ ∈ [0,1], where λ = 0 and 1 correspond to the purely supervised HMT model and purely unsupervised HMT-based clustering, respectively. We consider the homotopy method to track a path of fixed points starting from λ = 0, with the optimal source allocation identified as a critical transition point where the solution is unsupported by the initial labeled data. Experimental results on real textures demonstrate the superiority of this method compared to the EM-based semi-supervised HMT training. © 2006 IEEE.

Duke Authors

Cited Authors

  • Dasgupta, N; Shihao, J; Carin, L

Published Date

  • December 1, 2006

Published In

Volume / Issue

  • 2 /

International Standard Serial Number (ISSN)

  • 1520-6149

Citation Source

  • Scopus