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Image Annotation and Refinement with Markov Chain Model of Visual Keywords and the Semantics

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)

Abstract

This paper presents a discriminative stochastic method for image annotation and refinement. We first segmented the images into regions and then cluster them into visual blobs with a small number than the whole training image regions. Each visual blob is regarded as a key visual word. Given the training image set with annotations, we find that annotation process is conditioned by the selection sequence of both the semantic word and the key visual word. The process could be described in a Markov Chain with the transition process both between the candidate annotations and the visual words set. Experiments show the performance of this annotation method outperforms the state of art methods.

Keywords

Image annotation Markov Chain 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.College of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingChina

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