Skip to Main content Skip to Navigation
Conference papers

Classification of images based on Hidden Markov Models

Abstract : We propose to use hidden Markov models (HMMs) to classify images. Images are modeled by extracting symbols corresponding to 3x3 binary neighborhoods of interest points, and by ordering these symbols by decreasing saliency order, thus obtaining strings of symbols. HMMs are learned from sets of strings modeling classes of images. The method has been tested on the SIMPLIcity database and shows an improvement over competing approaches based on interest points. We also evaluate these approaches for classifying thumbnail images, i.e., low resolution images.
Document type :
Conference papers
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01437635
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Tuesday, March 24, 2020 - 2:25:50 PM
Last modification on : Wednesday, March 25, 2020 - 2:03:32 PM
Document(s) archivé(s) le : Thursday, June 25, 2020 - 1:47:45 PM

File

version_finale.pdf
Files produced by the author(s)

Identifiers

Citation

Marc Mouret, Christine Solnon, Christian Wolf. Classification of images based on Hidden Markov Models. IEEE Workshop on Content Based Multimedia Indexing, Jun 2009, Chania, Crète, Greece. pp.169-174, ⟨10.1109/CBMI.2009.22⟩. ⟨hal-01437635⟩

Share

Metrics

Record views

342

Files downloads

399