Semantic feature extraction using genetic programming in image retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Li:2004:ICPR,

• author = "Qingyong Li and Hong Hu and Zhongzhi Shi",
• title = "Semantic feature extraction using genetic programming in image retrieval",
• booktitle = "Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004",
• year = "2004",
• volume = "1",
• pages = "648--651",
• month = aug,
• keywords = "genetic algorithms, genetic programming, content-based retrieval, feature extraction, image retrieval, image texture, visual perception Tamura texture model, content based image retrieval, human visual perception, linguistic expression, semantic feature extraction, texture extraction, visual feature extraction",
• DOI = "doi:10.1109/ICPR.2004.1334248",
• size = "4 pages",
• abstract = "One of the big hurdles facing current content-based image retrieval (CBIR) is the semantic gap between the low-level visual features and the high-level semantic features. We proposed an approach to describe and extract the global texture semantic features. According to the Tamura texture model, we use the linguistic variable to describe the texture semantics, so it becomes possible to depict the image in linguistic expression such as coarse, fine. We use genetic programming to simulate the human visual perception and extract the semantic features value. Our experiments show that the semantic features have good accordance with the human perception, and also have good retrieval performance. In some extent, our approach bridges the semantic gap in CBIR.",
• notes = "also known as \cite{1334248}",

}

Genetic Programming entries for Qingyong Li Hong Hu Zhongzhi Shi

Citations