Image categorization based on clustering spatial frequency maps

Fuhui Long; Hanchuan Peng; David Dagan Feng

doi:10.1117/12.527284

18 December 2003 Image categorization based on clustering spatial frequency maps

Fuhui Long, Hanchuan Peng, David Dagan Feng

Proceedings Volume 5307, Storage and Retrieval Methods and Applications for Multimedia 2004; (2003) https://doi.org/10.1117/12.527284
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Image classification can facilitate semantic retrieval and browsing of large-scale image databases. Existing approaches are usually based on extracting local or global low-level features such as color, edge, and texture from images. In this paper, we propose an image categorization method that characterizes the respective scene structures in images. 2D Spatial Frequency Map of an image, as well as the respective projection vector representations and principal component representations, are used to characterize the spatial structure of the image. Based on multiple similarity scores, we use a spectral clustering method and a maximal-spanning-tree-spectral-clustering method to generate image categories.

Citation Download Citation

Fuhui Long, Hanchuan Peng, and David Dagan Feng "Image categorization based on clustering spatial frequency maps", Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); https://doi.org/10.1117/12.527284

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