A semantic description for content-based image retrieval

被引：6

作者：

Wang, Bing ^{[1
]}

Mang, Xin ^{[2
]}

Zhao, Xiao-Yan ^{[1
]}

Zang, Zhi-De ^{[1
]}

Zhang, Hong-Xia ^{[3
]}

机构：

[1] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Peoples R China

[2] Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China

[3] Hebei Coll Finance, Dept Informat Management & Engn, Baoding 071051, Peoples R China

来源：

PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年

基金：

中国国家自然科学基金;

关键词：

content-based image retrieval; image semantic model; semantic description; image clusters; SVM;

D O I：

10.1109/ICMLC.2008.4620822

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Robust and flexible semantic labeling of images is still a basic problem in content-based image representation and retrieval. In this paper, a self-organizing image description model (SID) was put forward for describing the image high-level semantic content. This model is a hierarchical architecture, which includes primitive image layer, image feature layer, image semantic layer, multi-level semantic pattern layer and semantic labeling layer. A semantic-based retrieval algorithm (SBRA) for image high-level semantic content retrieval was designed and implemented. The performance of all experimental image retrieval system is evaluated on a database of around 3000 images. The experimental results show that SID and SBRA are effective in describing image high-level semantic content and can provide flexible image description and efficient image retrieval performance.

引用

页码：2466 / +

页数：2

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