Paper Info
Title
Semantics-Based Image Retrieval by Region Saliency
Abstract
We propose a new approach for semantics-based image retrieval. We use color-texture classification to generate the codebook which is used to segment images into regions. The content of a region is characterized by its self-saliency and the lower-level features of the region, including color and texture. The context of regions in an image describes their relationships, which are related to their relative-saliencies. High-level (semantics-based) querying and query-by-example are supported on the basis of the content and context of image regions. The experimental results demonstrate the effectiveness of our approach.
Year
DOI
Venue
2002
10.1007/3-540-45479-9_4
CIVR
Keywords
Field
DocType
color-texture classification,semantics-based image retrieval,segment image,image region,new approach,region saliency,lower-level feature,image retrieval,query by example
Computer science,Salience (neuroscience),Image retrieval,Image segmentation,Artificial intelligence,Computer vision,Automatic image annotation,Pattern recognition,Information retrieval,Image texture,Semantics,Visual Word,Codebook
Conference
Volume
ISSN
ISBN
2383
0302-9743
3-540-43899-8
Citations 
PageRank 
References 
21
0.90
6
Authors
3
Name
Order
Citations
PageRank
Wei Wang1514.27
Yuqing Song218122.44
Aidong Zhang32970405.63