Multilevel image segmentation and object representation for content-based image retrieval

Pinar Duygulu; Fatos T. Yarman-Vural

doi:10.1117/12.410956

1 January 2001 Multilevel image segmentation and object representation for content-based image retrieval

Pinar Duygulu, Fatos T. Yarman-Vural

Proceedings Volume 4315, Storage and Retrieval for Media Databases 2001; (2001) https://doi.org/10.1117/12.410956
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

Due to the increasing demand and offer of the technology, the next generation of the image file formats will be more likely to store and retrieve images based on their semantic conte. Thus, an image should be segmented into 'meaningful' regions, each of which corresponds to an object and/or background. In this study, we propose a scheme for multi- level image segmentation, based on a simple descriptor, called 'the closest color in the same neighborhood'. The proposed scheme generates a stack of images without using any segmentation threshold. The stack of images is hierarchically ordered in a uniformity tree. The uniformity tree is then associated with a semantic tree, which is built by the user for content based representation. The experiments indicate superior results for retrieving images, which consist of few objects and a background.

Citation Download Citation

Pinar Duygulu and Fatos T. Yarman-Vural "Multilevel image segmentation and object representation for content-based image retrieval", Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); https://doi.org/10.1117/12.410956

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available