Evaluating Combinational Illumination Estimation Methods on Real-World Images
Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide bett...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on image processing 2014-03, Vol.23 (3), p.1194-1209 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1209 |
|---|---|
| container_issue | 3 |
| container_start_page | 1194 |
| container_title | IEEE transactions on image processing |
| container_volume | 23 |
| creator | Li, Bing Xiong, Weihua Hu, Weiming Funt, Brian |
| description | Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues. |
| doi_str_mv | 10.1109/TIP.2013.2277943 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TIP_2013_2277943</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6583331</ieee_id><sourcerecordid>1520975988</sourcerecordid><originalsourceid>FETCH-LOGICAL-c410t-4db9ad215e925e60540ca24d1283afd80b2bcc2b8f128c58c78bfad4da7f9a673</originalsourceid><addsrcrecordid>eNqNkU1r3DAQhkVpaNK090KhGEqhF29n9K1jWTbJQkJCSOnRyJKcOsh2atmF_PtoWTeBnHLSK-mZgZmHkE8IK0QwP262VysKyFaUKmU4e0OO0HAsATh9mzMIVSrk5pC8T-kOALlA-Y4cUmYUl5QfkYvNPxtnO7X9bbEeurrtcx56G4ttjHO3XItNmtpuHy_C9GfwqcjxOthY_h7G6IttZ29D-kAOGhtT-Licx-TXyeZmfVaeX55u1z_PS8cRppL72lhPUQRDRZAgODhLuUeqmW28hprWztFaN_nFCe2UrhvrubeqMVYqdky-7_vej8PfOaSp6trkQoy2D8OcKhQUjBJG61egKCTXHE1Gv75A74Z5zLvYUcAlk0pCpmBPuXFIaQxNdT_m3YwPFUK1s1JlK9XOSrVYySVflsZz3QX_VPBfQwa-LYBNzsZmtL1r0zOnuZESd3N_3nNtCOHpWwrNGEP2CPSCnHk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1504636760</pqid></control><display><type>article</type><title>Evaluating Combinational Illumination Estimation Methods on Real-World Images</title><source>IEEE Electronic Library (IEL)</source><creator>Li, Bing ; Xiong, Weihua ; Hu, Weiming ; Funt, Brian</creator><creatorcontrib>Li, Bing ; Xiong, Weihua ; Hu, Weiming ; Funt, Brian</creatorcontrib><description>Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2013.2277943</identifier><identifier>PMID: 23974624</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; automatic white balance ; Color ; color constancy ; Colorimetry - methods ; committee-based ; Cues ; Estimates ; Estimation ; Exact sciences and technology ; Geometry ; Illumination ; Illumination estimation ; Image color analysis ; Image edge detection ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Imaging, Three-Dimensional - methods ; Indoor ; Information theory ; Information, signal and communications theory ; Lighting ; Lighting - methods ; Methods ; Outdoor ; Regression ; Signal and communications theory ; Signal processing ; Signal representation. Spectral analysis ; Signal, noise ; Support vector machines ; Telecommunications and information theory ; Three dimensional ; Training</subject><ispartof>IEEE transactions on image processing, 2014-03, Vol.23 (3), p.1194-1209</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-4db9ad215e925e60540ca24d1283afd80b2bcc2b8f128c58c78bfad4da7f9a673</citedby><cites>FETCH-LOGICAL-c410t-4db9ad215e925e60540ca24d1283afd80b2bcc2b8f128c58c78bfad4da7f9a673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6583331$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6583331$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28496617$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23974624$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Bing</creatorcontrib><creatorcontrib>Xiong, Weihua</creatorcontrib><creatorcontrib>Hu, Weiming</creatorcontrib><creatorcontrib>Funt, Brian</creatorcontrib><title>Evaluating Combinational Illumination Estimation Methods on Real-World Images</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>automatic white balance</subject><subject>Color</subject><subject>color constancy</subject><subject>Colorimetry - methods</subject><subject>committee-based</subject><subject>Cues</subject><subject>Estimates</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Geometry</subject><subject>Illumination</subject><subject>Illumination estimation</subject><subject>Image color analysis</subject><subject>Image edge detection</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Indoor</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Lighting</subject><subject>Lighting - methods</subject><subject>Methods</subject><subject>Outdoor</subject><subject>Regression</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal representation. Spectral analysis</subject><subject>Signal, noise</subject><subject>Support vector machines</subject><subject>Telecommunications and information theory</subject><subject>Three dimensional</subject><subject>Training</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqNkU1r3DAQhkVpaNK090KhGEqhF29n9K1jWTbJQkJCSOnRyJKcOsh2atmF_PtoWTeBnHLSK-mZgZmHkE8IK0QwP262VysKyFaUKmU4e0OO0HAsATh9mzMIVSrk5pC8T-kOALlA-Y4cUmYUl5QfkYvNPxtnO7X9bbEeurrtcx56G4ttjHO3XItNmtpuHy_C9GfwqcjxOthY_h7G6IttZ29D-kAOGhtT-Licx-TXyeZmfVaeX55u1z_PS8cRppL72lhPUQRDRZAgODhLuUeqmW28hprWztFaN_nFCe2UrhvrubeqMVYqdky-7_vej8PfOaSp6trkQoy2D8OcKhQUjBJG61egKCTXHE1Gv75A74Z5zLvYUcAlk0pCpmBPuXFIaQxNdT_m3YwPFUK1s1JlK9XOSrVYySVflsZz3QX_VPBfQwa-LYBNzsZmtL1r0zOnuZESd3N_3nNtCOHpWwrNGEP2CPSCnHk</recordid><startdate>20140301</startdate><enddate>20140301</enddate><creator>Li, Bing</creator><creator>Xiong, Weihua</creator><creator>Hu, Weiming</creator><creator>Funt, Brian</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20140301</creationdate><title>Evaluating Combinational Illumination Estimation Methods on Real-World Images</title><author>Li, Bing ; Xiong, Weihua ; Hu, Weiming ; Funt, Brian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-4db9ad215e925e60540ca24d1283afd80b2bcc2b8f128c58c78bfad4da7f9a673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>automatic white balance</topic><topic>Color</topic><topic>color constancy</topic><topic>Colorimetry - methods</topic><topic>committee-based</topic><topic>Cues</topic><topic>Estimates</topic><topic>Estimation</topic><topic>Exact sciences and technology</topic><topic>Geometry</topic><topic>Illumination</topic><topic>Illumination estimation</topic><topic>Image color analysis</topic><topic>Image edge detection</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image processing</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Indoor</topic><topic>Information theory</topic><topic>Information, signal and communications theory</topic><topic>Lighting</topic><topic>Lighting - methods</topic><topic>Methods</topic><topic>Outdoor</topic><topic>Regression</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal representation. Spectral analysis</topic><topic>Signal, noise</topic><topic>Support vector machines</topic><topic>Telecommunications and information theory</topic><topic>Three dimensional</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Bing</creatorcontrib><creatorcontrib>Xiong, Weihua</creatorcontrib><creatorcontrib>Hu, Weiming</creatorcontrib><creatorcontrib>Funt, Brian</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Bing</au><au>Xiong, Weihua</au><au>Hu, Weiming</au><au>Funt, Brian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating Combinational Illumination Estimation Methods on Real-World Images</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2014-03-01</date><risdate>2014</risdate><volume>23</volume><issue>3</issue><spage>1194</spage><epage>1209</epage><pages>1194-1209</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>23974624</pmid><doi>10.1109/TIP.2013.2277943</doi><tpages>16</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1057-7149 |
| ispartof | IEEE transactions on image processing, 2014-03, Vol.23 (3), p.1194-1209 |
| issn | 1057-7149 1941-0042 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TIP_2013_2277943 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences automatic white balance Color color constancy Colorimetry - methods committee-based Cues Estimates Estimation Exact sciences and technology Geometry Illumination Illumination estimation Image color analysis Image edge detection Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Imaging, Three-Dimensional - methods Indoor Information theory Information, signal and communications theory Lighting Lighting - methods Methods Outdoor Regression Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Support vector machines Telecommunications and information theory Three dimensional Training |
| title | Evaluating Combinational Illumination Estimation Methods on Real-World Images |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-17T11%3A01%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluating%20Combinational%20Illumination%20Estimation%20Methods%20on%20Real-World%20Images&rft.jtitle=IEEE%20transactions%20on%20image%20processing&rft.au=Li,%20Bing&rft.date=2014-03-01&rft.volume=23&rft.issue=3&rft.spage=1194&rft.epage=1209&rft.pages=1194-1209&rft.issn=1057-7149&rft.eissn=1941-0042&rft.coden=IIPRE4&rft_id=info:doi/10.1109/TIP.2013.2277943&rft_dat=%3Cproquest_RIE%3E1520975988%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1504636760&rft_id=info:pmid/23974624&rft_ieee_id=6583331&rfr_iscdi=true |