Ontology type: schema:Chapter
2010
AUTHORSEmilie Dumont , Hervé Glotin , Sébastien Paris , Zhong-Qiu Zhao
ABSTRACTIn this paper we propose an original image retrieval model inspired from the vector space information retrieval model. We build for different features and different scales a visual concept dictionary composed by visual words intended to represent a semantic concept, and then we represent an image by the frequency of the visual words within the image. Then the image similarity is computed as in the textual domain where a textual document is represented by a vector in which each component is the frequency of occurrence of a specific textual word in that document. We then adapt the common text-based paradigm by using the TF-IDF weighting scheme to construct a WF-IIF weighting scheme in our Multi-Scale Visual Dictionary (MSVD) vector space model. The experiments are conducted on the 2009 Visual Concept Detection ImageCLEF Campaign. We compare WF-IIF to usual direct Support-Vector Machine (SVM) algorithm. We demonstrate that SVM and WF-IIF are in average over all the concept giving the same Area Under the Curve (AUC). We then discuss the fusion process that should enhance the whole system, and of some particular properties of MSVD, that shall be less dependant of the training set size of each concept than the SVM. More... »
PAGES299-306
Multilingual Information Access Evaluation II. Multimedia Experiments
ISBN
978-3-642-15750-9
978-3-642-15751-6
http://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_39
DOIhttp://dx.doi.org/10.1007/978-3-642-15751-6_39
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1048537843
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": {
"name": [
"Sciences and Information Lab. LSIS UMR CNRS 6168, France",
"University of Sud Toulon-Var, France"
],
"type": "Organization"
},
"familyName": "Dumont",
"givenName": "Emilie",
"type": "Person"
},
{
"affiliation": {
"name": [
"Sciences and Information Lab. LSIS UMR CNRS 6168, France",
"University of Sud Toulon-Var, France"
],
"type": "Organization"
},
"familyName": "Glotin",
"givenName": "Herv\u00e9",
"id": "sg:person.016622300103.82",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016622300103.82"
],
"type": "Person"
},
{
"affiliation": {
"name": [
"Sciences and Information Lab. LSIS UMR CNRS 6168, France"
],
"type": "Organization"
},
"familyName": "Paris",
"givenName": "S\u00e9bastien",
"id": "sg:person.014371661147.40",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014371661147.40"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Hefei University of Technology",
"id": "https://www.grid.ac/institutes/grid.256896.6",
"name": [
"College of Computer Science and Information Engineering, Hefei University of Technology, China"
],
"type": "Organization"
},
"familyName": "Zhao",
"givenName": "Zhong-Qiu",
"id": "sg:person.016102547154.53",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016102547154.53"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-642-15751-6_32",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005081003",
"https://doi.org/10.1007/978-3-642-15751-6_32"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-15751-6_32",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005081003",
"https://doi.org/10.1007/978-3-642-15751-6_32"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.fss.2004.07.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031236630"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1146/annurev.cs.04.060190.002221",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036425691"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-007-0036-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037308766",
"https://doi.org/10.1007/s10851-007-0036-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10851-007-0036-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037308766",
"https://doi.org/10.1007/s10851-007-0036-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-48762-x_46",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042663072",
"https://doi.org/10.1007/3-540-48762-x_46"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-48762-x_46",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042663072",
"https://doi.org/10.1007/3-540-48762-x_46"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-15751-6_10",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052874237",
"https://doi.org/10.1007/978-3-642-15751-6_10"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-15751-6_10",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052874237",
"https://doi.org/10.1007/978-3-642-15751-6_10"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11042-006-0033-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053479040",
"https://doi.org/10.1007/s11042-006-0033-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icip.2004.1421376",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094993035"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icip.2009.5413350",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095143406"
],
"type": "CreativeWork"
}
],
"datePublished": "2010",
"datePublishedReg": "2010-01-01",
"description": "In this paper we propose an original image retrieval model inspired from the vector space information retrieval model. We build for different features and different scales a visual concept dictionary composed by visual words intended to represent a semantic concept, and then we represent an image by the frequency of the visual words within the image. Then the image similarity is computed as in the textual domain where a textual document is represented by a vector in which each component is the frequency of occurrence of a specific textual word in that document. We then adapt the common text-based paradigm by using the TF-IDF weighting scheme to construct a WF-IIF weighting scheme in our Multi-Scale Visual Dictionary (MSVD) vector space model. The experiments are conducted on the 2009 Visual Concept Detection ImageCLEF Campaign. We compare WF-IIF to usual direct Support-Vector Machine (SVM) algorithm. We demonstrate that SVM and WF-IIF are in average over all the concept giving the same Area Under the Curve (AUC). We then discuss the fusion process that should enhance the whole system, and of some particular properties of MSVD, that shall be less dependant of the training set size of each concept than the SVM.",
"editor": [
{
"familyName": "Peters",
"givenName": "Carol",
"type": "Person"
},
{
"familyName": "Caputo",
"givenName": "Barbara",
"type": "Person"
},
{
"familyName": "Gonzalo",
"givenName": "Julio",
"type": "Person"
},
{
"familyName": "Jones",
"givenName": "Gareth J. F.",
"type": "Person"
},
{
"familyName": "Kalpathy-Cramer",
"givenName": "Jayashree",
"type": "Person"
},
{
"familyName": "M\u00fcller",
"givenName": "Henning",
"type": "Person"
},
{
"familyName": "Tsikrika",
"givenName": "Theodora",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-15751-6_39",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-642-15750-9",
"978-3-642-15751-6"
],
"name": "Multilingual Information Access Evaluation II. Multimedia Experiments",
"type": "Book"
},
"name": "A Fast Visual Word Frequency - Inverse Image Frequency for Detector of Rare Concepts",
"pagination": "299-306",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1048537843"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-15751-6_39"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"e739d36513f33c8881e961780df5986bd1bac8979b93a49137bd8a3a58c3b380"
]
}
],
"publisher": {
"location": "Berlin, Heidelberg",
"name": "Springer Berlin Heidelberg",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-15751-6_39",
"https://app.dimensions.ai/details/publication/pub.1048537843"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-16T08:23",
"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/v90/0000000363_0000000363/records_70037_00000002.jsonl",
"type": "Chapter",
"url": "https://link.springer.com/10.1007%2F978-3-642-15751-6_39"
}
]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.10.1007/978-3-642-15751-6_39'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_39'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_39'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_39'
This table displays all metadata directly associated to this object as RDF triples.
155 TRIPLES
23 PREDICATES
36 URIs
20 LITERALS
8 BLANK NODES