Probabilistic-based semantic image feature using visual words

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

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

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.

原文英語
主出版物標題Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
頁面386-389
頁數4
出版狀態已發佈 - 2009 十二月 1
事件11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, 日本
持續時間: 2009 五月 202009 五月 22

出版系列

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

其他

其他11th IAPR Conference on Machine Vision Applications, MVA 2009
國家/地區日本
城市Yokohama
期間2009/05/202009/05/22

ASJC Scopus subject areas

  • 電腦視覺和模式識別

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