Supervised texture classification: color space or texture feature selection?
Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the perform...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Porebski, A. [verfasserIn] Vandenbroucke, N. [verfasserIn] Macaire, L. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 16(2012), 1 vom: 24. Aug., Seite 1-18 |
|---|---|
| Übergeordnetes Werk: |
volume:16 ; year:2012 ; number:1 ; day:24 ; month:08 ; pages:1-18 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10044-012-0291-9 |
|---|
| Katalog-ID: |
SPR008213267 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR008213267 | ||
| 003 | DE-627 | ||
| 005 | 20201124023801.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201005s2012 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10044-012-0291-9 |2 doi | |
| 035 | |a (DE-627)SPR008213267 | ||
| 035 | |a (SPR)s10044-012-0291-9-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Porebski, A. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Supervised texture classification: color space or texture feature selection? |
| 264 | 1 | |c 2012 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. | ||
| 650 | 4 | |a Texture classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Color spaces |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Co-occurrence matrix |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Vandenbroucke, N. |e verfasserin |4 aut | |
| 700 | 1 | |a Macaire, L. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Pattern Analysis & Applications |d Springer-Verlag, 1999 |g 16(2012), 1 vom: 24. Aug., Seite 1-18 |w (DE-627)SPR008209189 |7 nnns |
| 773 | 1 | 8 | |g volume:16 |g year:2012 |g number:1 |g day:24 |g month:08 |g pages:1-18 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10044-012-0291-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 16 |j 2012 |e 1 |b 24 |c 08 |h 1-18 | ||
| author_variant |
a p ap n v nv l m lm |
|---|---|
| matchkey_str |
porebskiavandenbrouckenmacairel:2012----:uevsdetrcasfctoclrpcotxu |
| hierarchy_sort_str |
2012 |
| publishDate |
2012 |
| allfields |
10.1007/s10044-012-0291-9 doi (DE-627)SPR008213267 (SPR)s10044-012-0291-9-e DE-627 ger DE-627 rakwb eng Porebski, A. verfasserin aut Supervised texture classification: color space or texture feature selection? 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 Vandenbroucke, N. verfasserin aut Macaire, L. verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 16(2012), 1 vom: 24. Aug., Seite 1-18 (DE-627)SPR008209189 nnns volume:16 year:2012 number:1 day:24 month:08 pages:1-18 https://dx.doi.org/10.1007/s10044-012-0291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 16 2012 1 24 08 1-18 |
| spelling |
10.1007/s10044-012-0291-9 doi (DE-627)SPR008213267 (SPR)s10044-012-0291-9-e DE-627 ger DE-627 rakwb eng Porebski, A. verfasserin aut Supervised texture classification: color space or texture feature selection? 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 Vandenbroucke, N. verfasserin aut Macaire, L. verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 16(2012), 1 vom: 24. Aug., Seite 1-18 (DE-627)SPR008209189 nnns volume:16 year:2012 number:1 day:24 month:08 pages:1-18 https://dx.doi.org/10.1007/s10044-012-0291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 16 2012 1 24 08 1-18 |
| allfields_unstemmed |
10.1007/s10044-012-0291-9 doi (DE-627)SPR008213267 (SPR)s10044-012-0291-9-e DE-627 ger DE-627 rakwb eng Porebski, A. verfasserin aut Supervised texture classification: color space or texture feature selection? 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 Vandenbroucke, N. verfasserin aut Macaire, L. verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 16(2012), 1 vom: 24. Aug., Seite 1-18 (DE-627)SPR008209189 nnns volume:16 year:2012 number:1 day:24 month:08 pages:1-18 https://dx.doi.org/10.1007/s10044-012-0291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 16 2012 1 24 08 1-18 |
| allfieldsGer |
10.1007/s10044-012-0291-9 doi (DE-627)SPR008213267 (SPR)s10044-012-0291-9-e DE-627 ger DE-627 rakwb eng Porebski, A. verfasserin aut Supervised texture classification: color space or texture feature selection? 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 Vandenbroucke, N. verfasserin aut Macaire, L. verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 16(2012), 1 vom: 24. Aug., Seite 1-18 (DE-627)SPR008209189 nnns volume:16 year:2012 number:1 day:24 month:08 pages:1-18 https://dx.doi.org/10.1007/s10044-012-0291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 16 2012 1 24 08 1-18 |
| allfieldsSound |
10.1007/s10044-012-0291-9 doi (DE-627)SPR008213267 (SPR)s10044-012-0291-9-e DE-627 ger DE-627 rakwb eng Porebski, A. verfasserin aut Supervised texture classification: color space or texture feature selection? 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 Vandenbroucke, N. verfasserin aut Macaire, L. verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 16(2012), 1 vom: 24. Aug., Seite 1-18 (DE-627)SPR008209189 nnns volume:16 year:2012 number:1 day:24 month:08 pages:1-18 https://dx.doi.org/10.1007/s10044-012-0291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 16 2012 1 24 08 1-18 |
| language |
English |
| source |
Enthalten in Pattern Analysis & Applications 16(2012), 1 vom: 24. Aug., Seite 1-18 volume:16 year:2012 number:1 day:24 month:08 pages:1-18 |
| sourceStr |
Enthalten in Pattern Analysis & Applications 16(2012), 1 vom: 24. Aug., Seite 1-18 volume:16 year:2012 number:1 day:24 month:08 pages:1-18 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Texture classification Color spaces Feature selection Co-occurrence matrix |
| isfreeaccess_bool |
false |
| container_title |
Pattern Analysis & Applications |
| authorswithroles_txt_mv |
Porebski, A. @@aut@@ Vandenbroucke, N. @@aut@@ Macaire, L. @@aut@@ |
| publishDateDaySort_date |
2012-08-24T00:00:00Z |
| hierarchy_top_id |
SPR008209189 |
| id |
SPR008213267 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008213267</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023801.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10044-012-0291-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008213267</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10044-012-0291-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Porebski, A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Supervised texture classification: color space or texture feature selection?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Color spaces</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Co-occurrence matrix</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vandenbroucke, N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Macaire, L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern Analysis & Applications</subfield><subfield code="d">Springer-Verlag, 1999</subfield><subfield code="g">16(2012), 1 vom: 24. Aug., Seite 1-18</subfield><subfield code="w">(DE-627)SPR008209189</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:1</subfield><subfield code="g">day:24</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:1-18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10044-012-0291-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2012</subfield><subfield code="e">1</subfield><subfield code="b">24</subfield><subfield code="c">08</subfield><subfield code="h">1-18</subfield></datafield></record></collection>
|
| author |
Porebski, A. |
| spellingShingle |
Porebski, A. misc Texture classification misc Color spaces misc Feature selection misc Co-occurrence matrix Supervised texture classification: color space or texture feature selection? |
| authorStr |
Porebski, A. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR008209189 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Supervised texture classification: color space or texture feature selection? Texture classification (dpeaa)DE-He213 Color spaces (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Co-occurrence matrix (dpeaa)DE-He213 |
| topic |
misc Texture classification misc Color spaces misc Feature selection misc Co-occurrence matrix |
| topic_unstemmed |
misc Texture classification misc Color spaces misc Feature selection misc Co-occurrence matrix |
| topic_browse |
misc Texture classification misc Color spaces misc Feature selection misc Co-occurrence matrix |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Pattern Analysis & Applications |
| hierarchy_parent_id |
SPR008209189 |
| hierarchy_top_title |
Pattern Analysis & Applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR008209189 |
| title |
Supervised texture classification: color space or texture feature selection? |
| ctrlnum |
(DE-627)SPR008213267 (SPR)s10044-012-0291-9-e |
| title_full |
Supervised texture classification: color space or texture feature selection? |
| author_sort |
Porebski, A. |
| journal |
Pattern Analysis & Applications |
| journalStr |
Pattern Analysis & Applications |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2012 |
| contenttype_str_mv |
txt |
| container_start_page |
1 |
| author_browse |
Porebski, A. Vandenbroucke, N. Macaire, L. |
| container_volume |
16 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Porebski, A. |
| doi_str_mv |
10.1007/s10044-012-0291-9 |
| author2-role |
verfasserin |
| title_sort |
supervised texture classification: color space or texture feature selection? |
| title_auth |
Supervised texture classification: color space or texture feature selection? |
| abstract |
Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. |
| abstractGer |
Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. |
| abstract_unstemmed |
Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
1 |
| title_short |
Supervised texture classification: color space or texture feature selection? |
| url |
https://dx.doi.org/10.1007/s10044-012-0291-9 |
| remote_bool |
true |
| author2 |
Vandenbroucke, N. Macaire, L. |
| author2Str |
Vandenbroucke, N. Macaire, L. |
| ppnlink |
SPR008209189 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10044-012-0291-9 |
| up_date |
2024-07-03T18:00:33.512Z |
| _version_ |
1803581792993673216 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008213267</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023801.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10044-012-0291-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008213267</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10044-012-0291-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Porebski, A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Supervised texture classification: color space or texture feature selection?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Color spaces</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Co-occurrence matrix</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vandenbroucke, N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Macaire, L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern Analysis & Applications</subfield><subfield code="d">Springer-Verlag, 1999</subfield><subfield code="g">16(2012), 1 vom: 24. Aug., Seite 1-18</subfield><subfield code="w">(DE-627)SPR008209189</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:1</subfield><subfield code="g">day:24</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:1-18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10044-012-0291-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2012</subfield><subfield code="e">1</subfield><subfield code="b">24</subfield><subfield code="c">08</subfield><subfield code="h">1-18</subfield></datafield></record></collection>
|
| score |
7.3989286 |