SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm
The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory resul...
Gespeichert in:
| Veröffentlicht in: | Information processing letters Jg. 114; H. 6; S. 287 - 293 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
01.06.2014
Elsevier Sequoia S.A |
| Schlagworte: | |
| ISSN: | 0020-0190, 1872-6119 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms.
•The image segmentation based on the clustering can be formulated as an optimization problem.•We design a novel multiobjective optimization to solve the image segmentation.•The multi-state quantum bits are used to represent individuals.•A simple heuristic method is adopted to select a preferred solution from the final Pareto front.•Our proposed method is tested on one simulated SAR image and two real SAR images. |
|---|---|
| AbstractList | The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms. [PUBLICATION ABSTRACT] The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms. •The image segmentation based on the clustering can be formulated as an optimization problem.•We design a novel multiobjective optimization to solve the image segmentation.•The multi-state quantum bits are used to represent individuals.•A simple heuristic method is adopted to select a preferred solution from the final Pareto front.•Our proposed method is tested on one simulated SAR image and two real SAR images. The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms. |
| Author | Zhang, Xiangrong Jiao, Licheng Feng, Shixia Li, Yangyang |
| Author_xml | – sequence: 1 givenname: Yangyang surname: Li fullname: Li, Yangyang – sequence: 2 givenname: Shixia surname: Feng fullname: Feng, Shixia – sequence: 3 givenname: Xiangrong surname: Zhang fullname: Zhang, Xiangrong – sequence: 4 givenname: Licheng surname: Jiao fullname: Jiao, Licheng |
| BookMark | eNp9kMFq3DAQQEVJoZu0H9CboZde7M5Ia8umpxCapBAotMlZaOXxVkaWNpK8kL-vlu0ph5w0iPeG4V2yCx88MfYZoUHA7tvc2INrOKBokDeA8I5tsJe87hCHC7YB4FADDvCBXaY0A0C3FXLD9J_r35Vd9J6qRPuFfNbZBl_tdKKxKsPzqn1el9r6dLCx_C2rK8RuJpPtkSo6BreeFB1fKuPWlClav6-024do89_lI3s_aZfo0__3ij3d_ni8ua8fft39vLl-qI1o-1xLmMx2BDHJHndtN5lJo9SchrGDUQozSRqoFSR20HIAORlDU99xFHxs5XYQV-zree8hhueVUlaLTYac057CmhS2AkGUDm1Bv7xC57BGX64rFMgeetn1hZJnysSQUqRJGXuuk6O2TiGoU3o1q5JendIr5KqkLya-Mg-xNI4vbzrfzw6VRkdLUSVjyRsaS3WT1RjsG_Y_C1GfmQ |
| CODEN | IFPLAT |
| CitedBy_id | crossref_primary_10_1007_s00500_022_06916_0 crossref_primary_10_1016_j_asoc_2016_01_055 crossref_primary_10_1016_j_jpowsour_2015_09_052 crossref_primary_10_1038_s41598_019_48409_5 crossref_primary_10_1007_s40998_019_00251_1 crossref_primary_10_1080_01431161_2021_1954261 crossref_primary_10_3103_S014641161603007X crossref_primary_10_1016_j_asoc_2017_07_035 crossref_primary_10_1155_2019_3740586 crossref_primary_10_3390_math11092018 crossref_primary_10_1007_s11128_015_1195_6 crossref_primary_10_1109_MGRS_2020_3004508 crossref_primary_10_1109_TITS_2020_3025796 crossref_primary_10_1016_j_asoc_2018_06_030 crossref_primary_10_1016_j_measurement_2015_02_005 crossref_primary_10_1007_s10462_022_10280_8 crossref_primary_10_1155_2014_714657 crossref_primary_10_3233_JIFS_169102 crossref_primary_10_1155_2014_976202 crossref_primary_10_1016_j_jksuci_2021_12_023 |
| Cites_doi | 10.1002/asjc.160 10.1049/iet-cta.2008.0322 10.1109/TSMCB.2007.904544 10.1109/LGRS.2007.903065 10.1109/TSMC.1973.4309314 10.1109/TEVC.2006.877146 10.1109/TGRS.2008.918647 10.1109/TEVC.2002.804320 10.1016/j.ins.2011.02.025 10.1109/TGRS.2007.892604 10.1016/S0031-3203(99)00137-5 10.1109/LGRS.2007.903064 10.1109/4235.996017 10.1109/TSMCB.2008.927271 10.1109/TPAMI.2002.1114856 10.2307/2346830 10.1109/36.789624 10.1109/TNN.2011.2169426 10.1109/83.730380 10.1109/TSMCC.2008.919172 10.1109/LGRS.2010.2040800 |
| ContentType | Journal Article |
| Copyright | 2013 Elsevier B.V. Copyright Elsevier Sequoia S.A. Jun 2014 |
| Copyright_xml | – notice: 2013 Elsevier B.V. – notice: Copyright Elsevier Sequoia S.A. Jun 2014 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D 7U5 |
| DOI | 10.1016/j.ipl.2013.12.010 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts |
| DatabaseTitleList | Computer and Information Systems Abstracts Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-6119 |
| EndPage | 293 |
| ExternalDocumentID | 3248029091 10_1016_j_ipl_2013_12_010 S0020019013003190 |
| Genre | Feature |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFSI ABJNI ABMAC ABTAH ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BKOJK BKOMP BLXMC CS3 DU5 E.L EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HMJ HVGLF HZ~ IHE J1W KOM LG9 M26 M41 MO0 MS~ O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SME SPC SPCBC SSV SSZ T5K TN5 UQL WH7 WUQ XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 8FD JQ2 L7M L~C L~D 7U5 |
| ID | FETCH-LOGICAL-c358t-70fc4d03f781b56fcfa17a2e9d60d73cf7e9e53e3b052007fccef862132d57493 |
| ISICitedReferencesCount | 30 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000334485800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0190 |
| IngestDate | Thu Oct 02 19:18:57 EDT 2025 Sun Nov 09 07:05:52 EST 2025 Sat Nov 29 03:44:20 EST 2025 Tue Nov 18 22:09:45 EST 2025 Fri Feb 23 02:16:28 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Image segmentation Multiobjective clustering Quantum computing Algorithms SAR image |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c358t-70fc4d03f781b56fcfa17a2e9d60d73cf7e9e53e3b052007fccef862132d57493 |
| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
| PQID | 1507808768 |
| PQPubID | 45522 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_1531030025 proquest_journals_1507808768 crossref_citationtrail_10_1016_j_ipl_2013_12_010 crossref_primary_10_1016_j_ipl_2013_12_010 elsevier_sciencedirect_doi_10_1016_j_ipl_2013_12_010 |
| PublicationCentury | 2000 |
| PublicationDate | June 2014 2014-06-00 20140601 |
| PublicationDateYYYYMMDD | 2014-06-01 |
| PublicationDate_xml | – month: 06 year: 2014 text: June 2014 |
| PublicationDecade | 2010 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Information processing letters |
| PublicationYear | 2014 |
| Publisher | Elsevier B.V Elsevier Sequoia S.A |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Sequoia S.A |
| References | Jiao, Li, Gong (br0150) 2008; 38 Goh, Teoh, Tan (br0050) 2008; 9 Hartigan, Wong (br0010) 1979; 28 Awad, Chehdi (br0090) 2007; 4 Xia, He, Sun (br0100) 2007; 4 Zhang, Jiao, Liu (br0230) 2008; 46 Chen (br0030) 2009; 3 Bandyopadhyay, Maulik (br0210) 2007; 45 Chen, Li (br0020) 2008; 38 Saha, Bandyopadhyay (br0110) 2010; 7 Yang, Jiao, Gong (br0140) 2011; 181 Tatt, Isa (br0070) 2011; 22 Jin, Sendhoff (br0060) 2008; 38 Handl, Knowles (br0130) 2007; 11 Han, Kim (br0160) 2002; 6 Chen, Li (br0040) 2010; 12 Fukuda, Hirosawa (br0170) 1999; 37 Maulik, Bandyopadhyay (br0080) 2000; 33 Hasanzadeh, Kasaei (br0120) 2010; 7 Deb, Pratap, Agarwal (br0200) 2002; 6 Haralick, Shanmugan (br0180) 1973; 3 Haris, Efstratiadis, Maglaveras, Katsaggelos (br0190) 1998; 7 Maulik, Bandyopadhyay (br0220) 2002; 24 Maulik (10.1016/j.ipl.2013.12.010_br0080) 2000; 33 Handl (10.1016/j.ipl.2013.12.010_br0130) 2007; 11 Deb (10.1016/j.ipl.2013.12.010_br0200) 2002; 6 Chen (10.1016/j.ipl.2013.12.010_br0030) 2009; 3 Chen (10.1016/j.ipl.2013.12.010_br0040) 2010; 12 Saha (10.1016/j.ipl.2013.12.010_br0110) 2010; 7 Tatt (10.1016/j.ipl.2013.12.010_br0070) 2011; 22 Haralick (10.1016/j.ipl.2013.12.010_br0180) 1973; 3 Goh (10.1016/j.ipl.2013.12.010_br0050) 2008; 9 Jiao (10.1016/j.ipl.2013.12.010_br0150) 2008; 38 Chen (10.1016/j.ipl.2013.12.010_br0020) 2008; 38 Awad (10.1016/j.ipl.2013.12.010_br0090) 2007; 4 Han (10.1016/j.ipl.2013.12.010_br0160) 2002; 6 Bandyopadhyay (10.1016/j.ipl.2013.12.010_br0210) 2007; 45 Hasanzadeh (10.1016/j.ipl.2013.12.010_br0120) 2010; 7 Zhang (10.1016/j.ipl.2013.12.010_br0230) 2008; 46 Yang (10.1016/j.ipl.2013.12.010_br0140) 2011; 181 Fukuda (10.1016/j.ipl.2013.12.010_br0170) 1999; 37 Xia (10.1016/j.ipl.2013.12.010_br0100) 2007; 4 Hartigan (10.1016/j.ipl.2013.12.010_br0010) 1979; 28 Jin (10.1016/j.ipl.2013.12.010_br0060) 2008; 38 Haris (10.1016/j.ipl.2013.12.010_br0190) 1998; 7 Maulik (10.1016/j.ipl.2013.12.010_br0220) 2002; 24 |
| References_xml | – volume: 3 start-page: 610 year: 1973 end-page: 621 ident: br0180 article-title: Textural features for image classification publication-title: IEEE Trans. Syst. Man Cybern. – volume: 24 start-page: 1650 year: 2002 end-page: 1654 ident: br0220 article-title: Performance evaluation of some clustering algorithms and validity indices publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 7 year: 2010 ident: br0110 article-title: Application of a multiseed-based clustering technique for automatic satellite image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 181 start-page: 2797 year: 2011 end-page: 2812 ident: br0140 article-title: Artificial immune multi-objective SAR image segmentation with fused complementary features publication-title: Inf. Sci. – volume: 38 start-page: 258 year: 2008 end-page: 266 ident: br0020 article-title: Decentralized output-feedback neural control for systems with unknown interconnections publication-title: IEEE Trans. Syst. Man Cybern., Part B, Cybern. – volume: 12 start-page: 96 year: 2010 end-page: 102 ident: br0040 article-title: Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems publication-title: Asian J. Control – volume: 4 year: 2007 ident: br0090 article-title: Multicomponent image segmentation using a genetic algorithm and artificial natural network publication-title: IEEE Trans. Geosci. Remote Sens. Lett. – volume: 37 start-page: 2282 year: 1999 end-page: 2286 ident: br0170 article-title: A wavelet-based texture set applied to classification of multifrequency polarimetric SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 33 start-page: 1455 year: 2000 end-page: 1465 ident: br0080 article-title: Genetic algorithm-based clustering technique publication-title: Pattern Recognit. – volume: 4 year: 2007 ident: br0100 article-title: A rapid and automatic MRF-based clustering method for SAR images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 22 start-page: 1823 year: 2011 end-page: 1836 ident: br0070 article-title: Adaptive evolutionary artificial neural networks for pattern classification publication-title: IEEE Trans. Neural Netw. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: br0200 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 6 start-page: 580 year: 2002 end-page: 593 ident: br0160 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Trans. Evol. Comput. – volume: 11 start-page: 56 year: 2007 end-page: 76 ident: br0130 article-title: An evolutionary approach to multiobjective clustering publication-title: IEEE Trans. Evol. Comput. – volume: 3 start-page: 1383 year: 2009 end-page: 1394 ident: br0030 article-title: Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks publication-title: IET Control Theory Appl. – volume: 7 year: 2010 ident: br0120 article-title: A multispectral image segmentation method using size-weighted fuzzy clustering and membership connectedness publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 46 start-page: 2126 year: 2008 end-page: 2136 ident: br0230 article-title: Spectral clustering ensemble applied to texture features for SAR image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 28 start-page: 100 year: 1979 end-page: 108 ident: br0010 article-title: A k-means clustering algorithm publication-title: Appl. Stat. – volume: 38 start-page: 1234 year: 2008 end-page: 1253 ident: br0150 article-title: Quantum-inspired immune clonal algorithm for global optimization publication-title: IEEE Trans. Syst. Man Cybern., Part B, Cybern. – volume: 7 start-page: 1684 year: 1998 end-page: 1699 ident: br0190 article-title: Hybrid image segmentation using watersheds and fast region merging publication-title: IEEE Trans. Image Process. – volume: 9 start-page: 1531 year: 2008 end-page: 1548 ident: br0050 article-title: Hybrid multiobjective evolutionary design for artificial neural networks publication-title: IEEE Trans. Neural Netw. – volume: 45 start-page: 1506 year: 2007 end-page: 1511 ident: br0210 article-title: Multiobjective genetic clustering for pixel classification in remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 38 start-page: 397 year: 2008 end-page: 415 ident: br0060 article-title: Pareto-based multiobjective machine learning: an overview and case studies publication-title: IEEE Trans. Syst. Man Cybern., Part C, Appl. Rev. – volume: 12 start-page: 96 issue: 1 year: 2010 ident: 10.1016/j.ipl.2013.12.010_br0040 article-title: Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems publication-title: Asian J. Control doi: 10.1002/asjc.160 – volume: 3 start-page: 1383 issue: 10 year: 2009 ident: 10.1016/j.ipl.2013.12.010_br0030 article-title: Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks publication-title: IET Control Theory Appl. doi: 10.1049/iet-cta.2008.0322 – volume: 38 start-page: 258 issue: 1 year: 2008 ident: 10.1016/j.ipl.2013.12.010_br0020 article-title: Decentralized output-feedback neural control for systems with unknown interconnections publication-title: IEEE Trans. Syst. Man Cybern., Part B, Cybern. doi: 10.1109/TSMCB.2007.904544 – volume: 4 issue: 4 year: 2007 ident: 10.1016/j.ipl.2013.12.010_br0100 article-title: A rapid and automatic MRF-based clustering method for SAR images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2007.903065 – volume: 3 start-page: 610 issue: 6 year: 1973 ident: 10.1016/j.ipl.2013.12.010_br0180 article-title: Textural features for image classification publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1973.4309314 – volume: 11 start-page: 56 issue: 1 year: 2007 ident: 10.1016/j.ipl.2013.12.010_br0130 article-title: An evolutionary approach to multiobjective clustering publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2006.877146 – volume: 9 start-page: 1531 year: 2008 ident: 10.1016/j.ipl.2013.12.010_br0050 article-title: Hybrid multiobjective evolutionary design for artificial neural networks publication-title: IEEE Trans. Neural Netw. – volume: 7 issue: 2 year: 2010 ident: 10.1016/j.ipl.2013.12.010_br0110 article-title: Application of a multiseed-based clustering technique for automatic satellite image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 46 start-page: 2126 issue: 7 year: 2008 ident: 10.1016/j.ipl.2013.12.010_br0230 article-title: Spectral clustering ensemble applied to texture features for SAR image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2008.918647 – volume: 6 start-page: 580 issue: 6 year: 2002 ident: 10.1016/j.ipl.2013.12.010_br0160 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2002.804320 – volume: 181 start-page: 2797 issue: 13 year: 2011 ident: 10.1016/j.ipl.2013.12.010_br0140 article-title: Artificial immune multi-objective SAR image segmentation with fused complementary features publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.02.025 – volume: 45 start-page: 1506 issue: 5 year: 2007 ident: 10.1016/j.ipl.2013.12.010_br0210 article-title: Multiobjective genetic clustering for pixel classification in remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2007.892604 – volume: 33 start-page: 1455 issue: 9 year: 2000 ident: 10.1016/j.ipl.2013.12.010_br0080 article-title: Genetic algorithm-based clustering technique publication-title: Pattern Recognit. doi: 10.1016/S0031-3203(99)00137-5 – volume: 4 issue: 4 year: 2007 ident: 10.1016/j.ipl.2013.12.010_br0090 article-title: Multicomponent image segmentation using a genetic algorithm and artificial natural network publication-title: IEEE Trans. Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2007.903064 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.ipl.2013.12.010_br0200 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 38 start-page: 1234 issue: 5 year: 2008 ident: 10.1016/j.ipl.2013.12.010_br0150 article-title: Quantum-inspired immune clonal algorithm for global optimization publication-title: IEEE Trans. Syst. Man Cybern., Part B, Cybern. doi: 10.1109/TSMCB.2008.927271 – volume: 24 start-page: 1650 issue: 12 year: 2002 ident: 10.1016/j.ipl.2013.12.010_br0220 article-title: Performance evaluation of some clustering algorithms and validity indices publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2002.1114856 – volume: 28 start-page: 100 issue: 1 year: 1979 ident: 10.1016/j.ipl.2013.12.010_br0010 article-title: A k-means clustering algorithm publication-title: Appl. Stat. doi: 10.2307/2346830 – volume: 37 start-page: 2282 issue: 5 year: 1999 ident: 10.1016/j.ipl.2013.12.010_br0170 article-title: A wavelet-based texture set applied to classification of multifrequency polarimetric SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.789624 – volume: 22 start-page: 1823 issue: 11 year: 2011 ident: 10.1016/j.ipl.2013.12.010_br0070 article-title: Adaptive evolutionary artificial neural networks for pattern classification publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2011.2169426 – volume: 7 start-page: 1684 issue: 12 year: 1998 ident: 10.1016/j.ipl.2013.12.010_br0190 article-title: Hybrid image segmentation using watersheds and fast region merging publication-title: IEEE Trans. Image Process. doi: 10.1109/83.730380 – volume: 38 start-page: 397 issue: 3 year: 2008 ident: 10.1016/j.ipl.2013.12.010_br0060 article-title: Pareto-based multiobjective machine learning: an overview and case studies publication-title: IEEE Trans. Syst. Man Cybern., Part C, Appl. Rev. doi: 10.1109/TSMCC.2008.919172 – volume: 7 issue: 3 year: 2010 ident: 10.1016/j.ipl.2013.12.010_br0120 article-title: A multispectral image segmentation method using size-weighted fuzzy clustering and membership connectedness publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2010.2040800 |
| SSID | ssj0006437 |
| Score | 2.1911876 |
| Snippet | The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 287 |
| SubjectTerms | Algorithms Clustering Computer science Evolutionary Evolutionary algorithms Heuristic Image processing systems Image segmentation Mathematical models Mathematical problems Multiobjective clustering Optimization algorithms Quantum computing SAR image Simulation Studies Synthetic aperture radar |
| Title | SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm |
| URI | https://dx.doi.org/10.1016/j.ipl.2013.12.010 https://www.proquest.com/docview/1507808768 https://www.proquest.com/docview/1531030025 |
| Volume | 114 |
| WOSCitedRecordID | wos000334485800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6119 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006437 issn: 0020-0190 databaseCode: AIEXJ dateStart: 19950113 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Zj9MwELbKLg-8cCMKCzIS4oEqKLeTxwp1BauqIOhK5clyHaebKk2ybVOVX8DfZnwkPRCr5QFFiiLHOeT5PDOew4PQ25D7Pg9dARMp8CzfmzKLgSS3eAi6bOwCDCKuik2Q0SiaTOKvnc6vJhdmk5OiiLbbuPqvpIY2ILZMnf0HcrcvhQa4BqLDGcgO51sR_nv_Wy9byFCclZgtTGpR0ZPiKpGugesaBrNeWFkhnezQpmIKy-lcs76e2Jjfk_F0PK_lTgoqkzGflctsfbXY12dNNpP6QqVzDmTfXCUJter6UIUM_GDF7CczklJpn8ZUfZVts1Y6tBbsCQB3tix3_S8yVmozAgDNNBt7hePv4qra_AEZCqeLhLY8WGeSGrAdcFQjj7VwdnU5xT_4vjZBzD9klXQnOZ4y8Zp42YM9tkdf6PnlcEjHg8n4XXVtyfJj0k1varHcQacuCWLg8Kf9z4PJRSvUpX9TRwvpn28c5CpU8Oirf1NxjoS90mDGD9F9s_TAfQ2ZR6gjisfoQVPWAxsu_wQxQBBWCML7CMIKQRgujhGEDxGE9xGEdwjCLYKeosvzwfjjJ8tU4rC4F0Rri9gp9xPbSwmscoIw5SlzCHNFnIR2QjyeEhGLwBPeVG3jRVLORQprZcdzk4D4sfcMnRRlIZ4jDJMfDhnQagvfZmkUJRELmUscV8Db3C6ym8Gj3GxTL6ul5LSJR5xTGG8qx5s6LoXx7qL37SOV3qPlps5-QxFqlEytPFLA0k2PnTXUo2ayr6hcTEVyT8eoi960t4E_S6cbK0RZyz6qkh8sLV7cos9LdG83Z87QyXpZi1foLt-ss9XytQHlb4K5trk |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=SAR+image+segmentation+based+on+quantum-inspired+multiobjective+evolutionary+clustering+algorithm&rft.jtitle=Information+processing+letters&rft.au=Li%2C+Yangyang&rft.au=Feng%2C+Shixia&rft.au=Zhang%2C+Xiangrong&rft.au=Jiao%2C+Licheng&rft.date=2014-06-01&rft.issn=0020-0190&rft.volume=114&rft.issue=6&rft.spage=287&rft.epage=293&rft_id=info:doi/10.1016%2Fj.ipl.2013.12.010&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0190&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0190&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0190&client=summon |