Robust image classification based on a non-causal hidden Markov Gauss mixture model

We propose a novel image classification method using a non-causal hidden Markov Gauss mixture model (HMGMM) We apply supervised learning assuming that the observation probability distribution given each class can be estimated using Gauss mixture vector quantization (GMVQ) designed using the generali...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Kyungsuk Pyun, Chee Sun Won, Johan Lim, Gray, R.M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Gaussian distribution Gaussian processes Hidden Markov models Image classification Probability distribution Robustness State estimation Supervised learning Vector quantization
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