Performance analysis in content-based retrieval with textures

被引：0

作者：

Xu, K ^{[1
]}

Georgescu, B ^{[1
]}

Comaniciu, D ^{[1
]}

Meer, P ^{[1
]}

机构：

[1] Rutgers State Univ, Dept Elect & Comp Engn, Piscataway, NJ 08855 USA

来源：

15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS: APPLICATIONS, ROBOTICS SYSTEMS AND ARCHITECTURES | 2000年

关键词：

content-based retrieval; texture description; similarity measure;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The features employed in content-based retrieval are most often simple low-level representations, while a human observer judges similarity between images based on high-level semantic properties. Using textures as an example, we show that a more accurate description of the underlying distribution of low-level features does not improve the retrieval performance. We also introduce the simplified multiresolution symmetric autoregressive model for textures, and the Bhattacharyya distance based similarity measure. Experiments are per formed with four texture representations and four similarity measures over the Brodatz and VisTex databases.

引用

页码：275 / 278

页数：4