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
收藏
页码：2466 / +
页数：2
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