Efficient content-based image retrieval using automatic feature selection

Author

Swets, Daniel L. ; Weng, John J.

Author_Institution

Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA

fYear

1995

fDate

21-23 Nov 1995

Firstpage

85

Lastpage

90

Abstract

We describe a self-organizing framework for content-based retrieval of images from large image databases at the object recognition level. The system uses the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class

Keywords

feature extraction; object recognition; query processing; very large databases; visual databases; automatic feature selection; content-based image retrieval; hierarchical image database; large image databases; natural settings; object recognition; optimal feature selection; optimal projection; query technique; rapid retrieval rates; real-world objects; self-organizing framework; Computer science; Content based retrieval; Image databases; Image recognition; Image retrieval; Image storage; Information retrieval; Management information systems; Object recognition; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 1995. Proceedings., International Symposium on

Conference_Location

Coral Gables, FL

Print_ISBN

0-8186-7190-4

Type

conf

DOI

10.1109/ISCV.1995.476982

Filename

476982