Semantic clustering of images using patterns of relevance feedback

被引：0
|
作者：
Morrison, Donn [1 ]
Marchand-Maillet, Stephane [1 ]
Bruno, Eric [1 ]
机构：
[1] Univ Geneva, Comp Vision & Multimedia Lab, Geneva, Switzerland
来源：
2008 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING | 2008年
关键词：
image clustering; relevance feedback; long-term learning; latent semantic analysis;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
User-supplied data such as browsing logs, click-through data, and relevance feedback judgements are an important source of knowledge during semantic indexing of documents such as images and video. Low-level indexing and abstraction methods are limited in the manner with which semantic data can be dealt. In this paper and in the context of this semantic data, we apply latent semantic analysis on two forms of user-supplied data, real-world and artificially generated relevance feedback judgements in order to examine the validity of using artificially generated interaction data for the study of semantic image clustering.
引用
收藏
页码：307 / 313
页数：7
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