Probabilistic-based semantic image feature using visual words

Cheng Chieh Chiang, Jia Wei Wu, Greg C. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new image feature that is based on a semantic-level perspective in order to bridge the semantic gap between low-level features of images and high-level concepts of human perception. In this work, low-level image features are first quantized into a set of visual words, and then we apply probabilistic Latent Semantic Analysis model to automatically analyze what kinds of hidden concepts between visual words and images are involved. Therefore, we collect discovered concepts of an image and filter a part of unreliable concepts out to build a semantic-based image feature. We also discuss in detail how to define parameters for extracting the proposed feature. Several experiments are presented to show the efficiency of this work.

Original languageEnglish
Title of host publicationProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
Pages386-389
Number of pages4
Publication statusPublished - 2009 Dec 1
Event11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan
Duration: 2009 May 202009 May 22

Publication series

NameProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009

Other

Other11th IAPR Conference on Machine Vision Applications, MVA 2009
CountryJapan
CityYokohama
Period09/5/2009/5/22

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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