Categorizing Visual Contents by Matching Visual “Keywords” View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002-07-02

AUTHORS

Joo-Hwee Lim

ABSTRACT

In this paper, we propose a three-layer visual information processing architecture for extracting concise non-textual descriptions from visual contents. These coded descriptions capture both local saliencies and spatial configurations present in visual contents via prototypical visual tokens called visual “keywords”. Categorization of images and video shots represented by keyframes can be performed by comparing their coded descriptions. We demonstrate our proposed architecture in natural scene image categorization that outperforms methods which use aggregate measures of low-level features. More... »

PAGES

367-374

References to SciGraph publications

Book

TITLE

Visual Information and Information Systems

ISBN

978-3-540-66079-8
978-3-540-48762-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-48762-x_46

DOI

http://dx.doi.org/10.1007/3-540-48762-x_46

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042663072


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