STUDY ON THE CONTENT-BASED IMAGE RETRIEVAL SYSTEM BY UNSUPERVISED LEARNING

被引：2
|
作者：
Wang, Shuo [1 ]
Wang, Jun [2 ]
Wang, Bing [1 ]
Wang, Xue-Zheng [3 ]
机构：
[1] Hebei Univ, Fac Math & Comp Sci, Machine Learning Ctr, Baoding 071002, Peoples R China
[2] North China Elect Power Univ, Coll Energy & Engn, Baoding 071003, Peoples R China
[3] Polit Min 68307 Army, Fac Defending, Gansu 734000, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年
关键词：
Image retrieval system; Color feature; Texture feature; Normalized cut; Bipartition method; Minimum spanning tree; SEGMENTATION; PICTURES;
D O I：
10.1109/ICMLC.2009.5212152
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The content-based image retrieval (CBIR) system aims at searching and browsing the large image digital libraries based on automatically derived imagery features. This paper introduces two algorithms based on the normalized cut for images clustering. We extract the color and texture features for computing the distance between the images, and take advantage of the bipartition method and minimum spanning tree for grouping. The performance of this system using the above methods is evaluated on a database of around 8000 images from the internet. The searching accuracy is satisfied for the target requirement.
引用
收藏
页码：2324 / +
页数：2
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