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Texture features for clustering based multi-label classification of face images
Version 2 2024-06-04, 04:03
Version 1 2018-03-05, 15:21
conference contribution
posted on 2024-06-04, 04:03 authored by A Mohammed, R Xiamixiding, Atul SajjanharAtul Sajjanhar, J Chen, G NasierdingIn this paper, we propose approaches for representation of texture features to improve classification of face images. The proposed methods for texture representation use Local Binary Pattern based approaches on polar raster sampled face images. Face images from FERET and CK+ databases are classified using clustering for multi-label classification. The proposed methods for texture representation show an improvement for clustering-based multi-label classification of face images.
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Pagination
1-5Location
Shanghai, ChinaPublisher DOI
Start date
2017-10-14End date
2017-10-16ISBN-13
9781538619377Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
Li Q, Wang L, Zhou M, Sun L, Qiu S, Liu HTitle of proceedings
CISP-BMEI 2017 : Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and InformaticsEvent
Image and Signal Processing, BioMedical Engineering and Informatics. Congress (2017 : 10th : Shanghai, China)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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No categories selectedKeywords
texture analysisLocal Binary Patternface classificationmulti-label classificationclustering080106 Image Processing080109 Pattern Recognition and Data Mining890399 Information Services not elsewhere classifiedSchool of Information Technology4603 Computer vision and multimedia computation4604 Cybersecurity and privacy
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