Image classification by a two dimensional hidden Markov model

Jia Li, Amir Najmi, Robert M. Gray

Research output: Contribution to journalConference article

12 Scopus citations

Abstract

Traditional block-based image classification algorithms, such as CART and VQ based classification, ignore the statistical dependency among image blocks. Consequently, these algorithms often suffer from over-localization. In order to benefit from the inter-block dependency, an image classification algorithm based on a hidden Markov model (HMM) is developed. An HMM for image classification, a two dimensional extension from the one dimensional HMM used for speech recognition, has transition probabilities conditioned on the states of neighboring blocks from both directions. Thus, the dependency in two dimensions can be reflected simultaneously. The HMM parameters are estimated by the EM algorithm. A two dimensional version of the Viterbi algorithm is also developed to classify optimally an image based on the trained HMM. An application of the HMM algorithm to document image and aerial image segmentation shows that the algorithm performs better than CART.

Original languageEnglish (US)
Pages (from-to)3313-3316
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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