Experiments on High Resolution Images Towards Outdoor Scene Classification

A Monadjemi, BT Thomas, M Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

We examine the use of high frequency features in high resolution images to increase texture classification accuracy when used in combination with lower frequency features. We used Gabor features derived from sections of 4032 2688 images. A neural network classifier was used to determine the classification performance of lower and high frequency features when used separately and then in combination. Feature shuffling and Principal Component Analysis was applied to determine both the role of each feature in the classification and to extract a smaller reduced feature set involving both lower and high frequency features.
Translated title of the contributionExperiments on High Resolution Images Towards Outdoor Scene Classification
Original languageEnglish
Title of host publicationUnknown
EditorsHorst Wildenauer, Walter Kropatsch
PublisherVienna University of Technology
Pages325 - 334
Number of pages9
Publication statusPublished - Feb 2002

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the Seventh Computer Vision Winter Workshop

Fingerprint Dive into the research topics of 'Experiments on High Resolution Images Towards Outdoor Scene Classification'. Together they form a unique fingerprint.

Cite this