Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval

被引：26
|
作者：
Nezambadi-pour, Hossein [1 ,2 ]
Kabir, Ehsanollah [1 ]
机构：
[1] Tarbiat Modares Univ, Dept Elect Engn, Tehran, Iran
[2] Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman, Iran
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 03期
关键词：
Semantic feature; Semantic network; Fuzzy classification; Relevance feedback; Content-based image retrieval; COLOR; SEMANTICS; FEATURES;
D O I：
10.1016/j.eswa.2008.07.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new method for combining visual and semantic features in image retrieval is presented. A fuzzy k-NN classifier assigns initial semantic labels to database images. These labels are gradually modified by relevance feedbacks from the users. Experimental results on a database of 1000 images from 10 semantic groups are reported. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：5948 / 5954
页数：7
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