@inproceedings{yapi_multi-band_2022,

title = {Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions},

author = {D. Yapi and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143638804&doi=10.1109%2fICPR56361.2022.9956448&partnerID=40&md5=3fe34da98f314b85014059630e4c8c6c},

doi = {10.1109/ICPR56361.2022.9956448},

isbn = {978-1-66549-062-7},

year  = {2022},

date = {2022-01-01},

booktitle = {Proceedings - International Conference on Pattern Recognition},

volume = {2022-August},

pages = {464–469},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables a compact and accurate representation of joint statistics of different sub-bands of multireslotion texture transform. This representation expresses correlation between sub-bands at different scales and orientations, and also between adjacent locations within the same subbands, providing a precise description of the texture layout. It enables also to combine different multi-scale transforms to build a richer and more representative texture signature. We successfully tested the model on traditional texture transforms such as wavelets and contourlets. Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-art methods. © 2022 IEEE.},

note = {ISSN: 10514651},

keywords = {Color texture retrieval, Finite mixtures, Gaussian distribution, Gaussians, Image retrieval, Image texture, Mixture of multivariate generalized gaussians, Multi band, Multi-scale Decomposition, Multivariate generalized gaussian distributions, Statistic modeling, Subbands, Texture models, Textures},

pubstate = {published},

tppubtype = {inproceedings}

}