Ontology type: schema:Chapter
2015
AUTHORSSalem Said , Lionel Bombrun , Yannick Berthoumieu
ABSTRACTThe current paper introduces new prior distributions on the zero-mean multivariate Gaussian model, with the aim of applying them to the classification of covariance matrices populations. These new prior distributions are entirely based on the Riemannian geometry of the multivariate Gaussian model. More precisely, the proposed Riemannian Gaussian distribution has two parameters, the centre of mass \(\bar{Y}\) and the dispersion parameter \(\sigma \). Its density with respect to Riemannian volume is proportional to \(\exp (-d^2(Y; \bar{Y}))\), where \(d^2(Y; \bar{Y})\) is the square of Rao’s Riemannian distance. We derive its maximum likelihood estimators and propose an experiment on the VisTex database for the classification of texture images. More... »
PAGES371-378
Geometric Science of Information
ISBN
978-3-319-25039-7
978-3-319-25040-3
http://scigraph.springernature.com/pub.10.1007/978-3-319-25040-3_40
DOIhttp://dx.doi.org/10.1007/978-3-319-25040-3_40
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1030593140
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University of Bordeaux",
"id": "https://www.grid.ac/institutes/grid.412041.2",
"name": [
"Laboratoire IMS, CNRS - UMR 5218, Universit\u00e9 de Bordeaux"
],
"type": "Organization"
},
"familyName": "Said",
"givenName": "Salem",
"id": "sg:person.016161076243.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016161076243.21"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Bordeaux",
"id": "https://www.grid.ac/institutes/grid.412041.2",
"name": [
"Laboratoire IMS, CNRS - UMR 5218, Universit\u00e9 de Bordeaux"
],
"type": "Organization"
},
"familyName": "Bombrun",
"givenName": "Lionel",
"id": "sg:person.010253405443.59",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010253405443.59"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Bordeaux",
"id": "https://www.grid.ac/institutes/grid.412041.2",
"name": [
"Laboratoire IMS, CNRS - UMR 5218, Universit\u00e9 de Bordeaux"
],
"type": "Organization"
},
"familyName": "Berthoumieu",
"givenName": "Yannick",
"id": "sg:person.010636166221.26",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010636166221.26"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.3390/e16074015",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002869950"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4612-3820-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008241329",
"https://doi.org/10.1007/978-1-4612-3820-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4612-3820-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008241329",
"https://doi.org/10.1007/978-1-4612-3820-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tit.2009.2016067",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015666916"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-006-6897-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020319281",
"https://doi.org/10.1007/s10851-006-6897-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-006-6897-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020319281",
"https://doi.org/10.1007/s10851-006-6897-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-22092-0_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032586168",
"https://doi.org/10.1007/978-3-642-22092-0_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-22092-0_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032586168",
"https://doi.org/10.1007/978-3-642-22092-0_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-006-6228-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049137733",
"https://doi.org/10.1007/s10851-006-6228-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-006-6228-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049137733",
"https://doi.org/10.1007/s10851-006-6228-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tbme.2011.2172210",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061528600"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tip.2002.804262",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061640766"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icip.2015.7351448",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095177422"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icip.2014.7025893",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095285020"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1090/gsm/034",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098730554"
],
"type": "CreativeWork"
}
],
"datePublished": "2015",
"datePublishedReg": "2015-01-01",
"description": "The current paper introduces new prior distributions on the zero-mean multivariate Gaussian model, with the aim of applying them to the classification of covariance matrices populations. These new prior distributions are entirely based on the Riemannian geometry of the multivariate Gaussian model. More precisely, the proposed Riemannian Gaussian distribution has two parameters, the centre of mass \\(\\bar{Y}\\) and the dispersion parameter \\(\\sigma \\). Its density with respect to Riemannian volume is proportional to \\(\\exp (-d^2(Y; \\bar{Y}))\\), where \\(d^2(Y; \\bar{Y})\\) is the square of Rao\u2019s Riemannian distance. We derive its maximum likelihood estimators and propose an experiment on the VisTex database for the classification of texture images.",
"editor": [
{
"familyName": "Nielsen",
"givenName": "Frank",
"type": "Person"
},
{
"familyName": "Barbaresco",
"givenName": "Fr\u00e9d\u00e9ric",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-319-25040-3_40",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-319-25039-7",
"978-3-319-25040-3"
],
"name": "Geometric Science of Information",
"type": "Book"
},
"name": "Texture Classification Using Rao\u2019s Distance on the Space of Covariance Matrices",
"pagination": "371-378",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-319-25040-3_40"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"91bdf4fb8ece2c9352913be35cd3380a97a4d926c94f3ba2d3532c1209f83b28"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1030593140"
]
}
],
"publisher": {
"location": "Cham",
"name": "Springer International Publishing",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-319-25040-3_40",
"https://app.dimensions.ai/details/publication/pub.1030593140"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-15T12:32",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8663_00000262.jsonl",
"type": "Chapter",
"url": "http://link.springer.com/10.1007/978-3-319-25040-3_40"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-25040-3_40'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-25040-3_40'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-25040-3_40'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-25040-3_40'
This table displays all metadata directly associated to this object as RDF triples.
121 TRIPLES
23 PREDICATES
38 URIs
20 LITERALS
8 BLANK NODES