Paper Info
Title
Hierarchical architecture for content-based image retrieval of paleontology images
Abstract
In this article a research work in the field of content-based multiresolution indexing and retrieval of images is presented. Our method uses multiresolution decomposition of images using wavelets - in the HSV colorspace - to extract parameters at multiple scales allowing a progressive (coarse-to-fine) retrieval process. Features are automatically classified into several clusters with K-means algorithm. A model image is computed for each cluster in order to represent all the images of this cluster. The process is reiterated again and again and each cluster is sub-divided into sub-clusters. The model images are stored in a tree which is proposed to users for browsing the database. The nodes of the tree are the families and the leaves are the images of the database. A paleontology images database is used to test the proposed technique. This kind of approach permits to build a visual interface easy to use for users. Our main contribution is the building of the tree with multiresolution indexing and retrieval of images and the generation of model images to be proposed to users.
Year
DOI
Venue
2002
10.1117/12.451084
Proceedings of SPIE
Keywords
Field
DocType
multiresolution analysis,K-means algorithm,classification,content-based image retrieval,image database
k-means clustering,Computer vision,Paleontology,Automatic image annotation,Computer science,Search engine indexing,Image retrieval,Multiresolution analysis,Artificial intelligence,Content-based image retrieval,Visual Word,Wavelet
Conference
Volume
ISSN
Citations 
4676
0277-786X
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Jérôme Landré1133.04
Frédéric Truchetet212118.97