Nonparametric multichannel texture description with simple spatial operators. M, O., T., &., P. In 1998. abstract bibtex A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggr egate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with Brodatz textures and MeasTex test suites the proposed method performs favorably com pared to GLCM, Gabor and GMRF features.
@inProceedings{
title = {Nonparametric multichannel texture description with simple spatial operators.},
type = {inProceedings},
year = {1998},
id = {d80a1cf7-0632-39f0-a040-85c6ac00e07e},
created = {2019-11-19T13:01:32.583Z},
file_attached = {false},
profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
last_modified = {2019-11-19T13:45:24.883Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {mvg:30},
source_type = {inproceedings},
notes = {Proc. 14th International Conference on Pattern Recognition, Brisbane, Australia, 1052 - 1056.},
private_publication = {false},
abstract = {A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggr egate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with Brodatz textures and MeasTex test suites the proposed method performs favorably com pared to GLCM, Gabor and GMRF features.},
bibtype = {inProceedings},
author = {M, Ojala T & Pietikäinen}
}
Downloads: 0
{"_id":"uwWv8hxQpJdW24zXe","bibbaseid":"m-nonparametricmultichanneltexturedescriptionwithsimplespatialoperators-1998","authorIDs":[],"author_short":["M, O., T., &., P."],"bibdata":{"title":"Nonparametric multichannel texture description with simple spatial operators.","type":"inProceedings","year":"1998","id":"d80a1cf7-0632-39f0-a040-85c6ac00e07e","created":"2019-11-19T13:01:32.583Z","file_attached":false,"profile_id":"bddcf02d-403b-3b06-9def-6d15cc293e20","group_id":"17585b85-df99-3a34-98c2-c73e593397d7","last_modified":"2019-11-19T13:45:24.883Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"mvg:30","source_type":"inproceedings","notes":"Proc. 14th International Conference on Pattern Recognition, Brisbane, Australia, 1052 - 1056.","private_publication":false,"abstract":"A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggr egate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with Brodatz textures and MeasTex test suites the proposed method performs favorably com pared to GLCM, Gabor and GMRF features.","bibtype":"inProceedings","author":"M, Ojala T & Pietikäinen","bibtex":"@inProceedings{\n title = {Nonparametric multichannel texture description with simple spatial operators.},\n type = {inProceedings},\n year = {1998},\n id = {d80a1cf7-0632-39f0-a040-85c6ac00e07e},\n created = {2019-11-19T13:01:32.583Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n group_id = {17585b85-df99-3a34-98c2-c73e593397d7},\n last_modified = {2019-11-19T13:45:24.883Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {mvg:30},\n source_type = {inproceedings},\n notes = {Proc. 14th International Conference on Pattern Recognition, Brisbane, Australia, 1052 - 1056.},\n private_publication = {false},\n abstract = {A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggr egate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with Brodatz textures and MeasTex test suites the proposed method performs favorably com pared to GLCM, Gabor and GMRF features.},\n bibtype = {inProceedings},\n author = {M, Ojala T & Pietikäinen}\n}","author_short":["M, O., T., &., P."],"bibbaseid":"m-nonparametricmultichanneltexturedescriptionwithsimplespatialoperators-1998","role":"author","urls":{},"downloads":0},"bibtype":"inProceedings","creationDate":"2019-11-19T13:17:06.689Z","downloads":0,"keywords":[],"search_terms":["nonparametric","multichannel","texture","description","simple","spatial","operators","m"],"title":"Nonparametric multichannel texture description with simple spatial operators.","year":1998}