Effective browsing of image search results via diversified visual summarization by clustering and refining clusters
The unprecedented growth in applications making use of digital images and multimedia references raised the requirement for image and topic search. Systematic processing of this information is a basic prerequisite for effective analysis of this information, as well as organization and management of i...
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| Published in | Signal, image and video processing Vol. 8; no. 4; pp. 699 - 721 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.05.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1863-1703 1863-1711 |
| DOI | 10.1007/s11760-013-0587-2 |
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| Abstract | The unprecedented growth in applications making use of digital images and multimedia references raised the requirement for image and topic search. Systematic processing of this information is a basic prerequisite for effective analysis of this information, as well as organization and management of it. Likewise, large collections of images are available on the Web, and many search engines provide the possibility of Web image searching based on the keywords. However, there are some problems in finding the desirable image according to the user needs. These problems include inexpressiveness of queries in description of user requirement, large number of irrelevant images to the intended search, lack of summarization, time-consuming review of overall images and lack of diversity. Clustering of image search results can be an efficient solution in solving these problems. In this work, several Folding-based algorithms have been proposed for clustering of image search results. In these algorithms, the efficiency, in comparison with Folding algorithm, is improved through a more effective selection of cluster’s representatives, fuzziness, weight and utilization of hierarchical algorithm preferences. In the represented system, in order to fit clusters with data more appropriately, an algorithm is proposed for refining the clusters. The proposed clustering causes a more convenient task in retrieval process for the user and also causes the efficient retrieval of images. According to the experiences, the proposed method improves the acceptable precision of image clustering. |
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| AbstractList | The unprecedented growth in applications making use of digital images and multimedia references raised the requirement for image and topic search. Systematic processing of this information is a basic prerequisite for effective analysis of this information, as well as organization and management of it. Likewise, large collections of images are available on the Web, and many search engines provide the possibility of Web image searching based on the keywords. However, there are some problems in finding the desirable image according to the user needs. These problems include inexpressiveness of queries in description of user requirement, large number of irrelevant images to the intended search, lack of summarization, time-consuming review of overall images and lack of diversity. Clustering of image search results can be an efficient solution in solving these problems. In this work, several Folding-based algorithms have been proposed for clustering of image search results. In these algorithms, the efficiency, in comparison with Folding algorithm, is improved through a more effective selection of cluster’s representatives, fuzziness, weight and utilization of hierarchical algorithm preferences. In the represented system, in order to fit clusters with data more appropriately, an algorithm is proposed for refining the clusters. The proposed clustering causes a more convenient task in retrieval process for the user and also causes the efficient retrieval of images. According to the experiences, the proposed method improves the acceptable precision of image clustering. |
| Author | Alamdar, Fatemeh Keyvanpour, Mohammad Reza |
| Author_xml | – sequence: 1 givenname: Fatemeh surname: Alamdar fullname: Alamdar, Fatemeh organization: Department of Computer Engineering, Alzahra University – sequence: 2 givenname: Mohammad Reza surname: Keyvanpour fullname: Keyvanpour, Mohammad Reza email: keyvanpour@alzahra.ac.ir organization: Department of Computer Engineering, Alzahra University |
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| CitedBy_id | crossref_primary_10_1007_s10115_021_01650_9 crossref_primary_10_1007_s00530_023_01099_6 crossref_primary_10_1016_j_compeleceng_2016_04_003 crossref_primary_10_1016_j_ipm_2015_12_005 |
| Cites_doi | 10.1145/1459359.1459448 10.1007/978-3-642-04617-9_20 10.1109/ICTAI.2004.50 10.1145/1026711.1026726 10.1109/76.927424 10.1145/1076034.1076120 10.1109/ICCV.2005.142 10.1007/s00530-010-0224-7 10.1145/1027527.1027747 10.1109/TPAMI.2008.121 10.1145/1101149.1101167 10.1016/j.patrec.2004.03.008 10.1109/34.730558 10.1016/j.jmva.2006.11.013 10.1145/1526709.1526756 10.1145/957013.957094 10.2307/2257960 10.1145/1386352.1386390 10.1016/j.proenv.2011.09.126 10.1117/12.205668 10.1007/978-3-642-15405-8_14 10.1109/DEXA.2008.113 10.1109/TSMC.1978.4309999 10.1007/BF02294245 10.1007/s11760-007-0049-9 10.1016/S0921-8890(97)00046-8 10.1007/978-3-662-05300-3 10.1109/ICICISYS.2010.5658548 10.1016/0031-3203(93)90145-M 10.1023/A:1012801612483 10.1007/s00530-004-0141-8 10.1080/01621459.1983.10478008 10.1145/1180639.1180743 10.1007/s00530-006-0020-6 10.1145/1460096.1460109 10.1109/TIP.2009.2035866 |
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| Keywords | Effective browsing Image clustering Feature extraction Folding algorithm Image search engine Refining of clusters Visual diversity |
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| References | TamuraHMoriSYamawakiTTextural features corresponding visual perceptionIEEE Trans. Syst. Man Cybern.1978846047310.1109/TSMC.1978.4309999 Alamdar, F., bahmani, Z., Haratizadeh, S.: Color quantization with clustering By F-PSO-GA. In: Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), China, pp. 233–238 (2010) Liu, H., Xie, X., Tang, X., Li, Z., Ma, W.Y.: Effective browsing of web image search results. In: Proceedings of the Multimedia, Information Retrieval, pp. 84–90 (2004) HalkidiMBaistakisYVazirgiannisMOn clustering validation techniquesJ. Intell. Inf. Syst.20011710714510.1023/A:10128016124830998.68154 Salvador, S., Chan, P.: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 576–584 (2004) WilliamsWLambertJMultivariate methods in plant ecology: V. Similarity analyses and information-analysisJ. Ecol.19665442744510.2307/2257960 MilliganGCooperMAn examination of procedures for determining the number of clusters in a data setPsychometrika19855015917910.1007/BF02294245 MeilăMComparing clusterings: an information based distanceJ. Multivar. Anal.20079887389510.1016/j.jmva.2006.11.01305170498 LammingDCronly-DillonJContrast sensitivity Chapter 5Vision and Visual Dysfunction1991LondonMacmillan Press van Leuken, R.H., Garcia, L., Olivares, X.: Visual Diversification of Image Search Result. In: Proceedings of the 18th International Conference on World Wide Web, ACM, USA, pp. 341–350 (2009) Jia, Y., Wang, J., Zhang, C., Hua, X.S.: Finding image exemplars using fast sparse affinity propagation. In: Proceedings of the ACM Multimedia, pp. 639–642 (2008) TianXTaoDHuaXSWuXActive reranking for web image searchIEEE Trans. Image Process.20101980582010.1109/TIP.2009.20358662756572 Ma, Y., Zhang, H.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the Eleventh ACM International Conference on Multimedia, Berkeley, CA, USA (2003) WangXWangYWangLImproving fuzzy c-means clustering based on feature-weight learningPattern Recogn. Lett.2004251123113210.1016/j.patrec.2004.03.008 DuranBOdellPCluster Analysis: A Survey, Volume 100 of Lecture Notes in Economics and Mathematical Systems1974BerlinSpringer WangJJiaLHuaXSInteractive browsing via diversified visual summarization for image search resultsMultimed. Syst.20101737939110.1007/s00530-010-0224-7 Deselaers, T., Keysers, D., Ney, H.: Clustering Visually Similar Images to Improve Image Search Engines. In: Proceedings of the Informatiktage 2003 der Gesellschaft für Informatik, Germany (2003) AlamdarF.KeyvanpourM.R.Effects of quaghistogram on image retrieval and clusteringInt. J. Comput. Sci. Appl.20124141153 Hirota, M., Yokoyama, S., Fukuta, N., Ishikawa, H.: Constraint-based clustering of image search results using photo metadata and low-level image features. In: Proceedings of the Computer and Information Science. Springer, Berlin, Heidelberg, pp. 165–178 (2010) Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from Google’s image search. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV), pp. 1816–1823 (2005) Moëllic, P.A. , Haugeard, J.E. , Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: Proceedings of the CIVR, pp. 269–278 (2008) NieburEKochCParasuramanRComputational architectures for attentionThe Attentive Brain1998Cambridge, MAMIT Press163186 Ahmad, S.: VISIT: a neural model of covert attention. Adv. Neural Inf. Process. Syst. 4, 420–427 (1991) ShenHTTanKZhouXCuiBICICLE: a semantic-based retrieval system for WWW imagesMultimed. Syst.20061143845410.1007/s00530-006-0020-6 EidenbergerHStatistical analysis of content-based MPEG-7 descriptors for image retrievalMultimed. Syst.200410849710.1007/s00530-004-0141-8 van Zwol, R., Murdock, V., Garcia, L., Ramirez, G.: Diversifying image search with user generated content. In: Proceedings of the International ACM Conference on Multimedia Information Retrieval, Canada (2008) WangHMissuraOGärtnerTWrobelSContext-Based Clustering of Image Search ResultsLect. Notes Comput. Sci.2009580315316010.1007/978-3-642-04617-9_20 GuldoganEGabboujMFeature selection for content-based image retrievalSignal Image Video Process.20082324125010.1007/s11760-007-0049-91151.68717 HassanzadehH.KeyvanpourM.R.Semantic web requirements through web mining techniquesInt. J. Comput. Theory Eng.20124616620 Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., Ma, W.Y.: Improving web search results using affinity graph. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, USA, pp. 504–511 (2005) FowlkesEMallowsCA method for comparing two hierarchical clusteringsJ. Am. Stat. Assoc.19837855356910.1080/01621459.1983.104780080545.62042 BobergJSalakoskiTGeneral formulation and evaluation of agglomerative clustering methods with metric and non-metric distancesPattern Recogn.1993261395140610.1016/0031-3203(93)90145-M BalujaSPomerleauDAExpectation-based selective attention for visual monitoring and control of a robot vehicleRobot. Auton. Syst.19972232934410.1016/S0921-8890(97)00046-8 Asbaghi, Sh., Keyvanpour, M.R., Amiri, A.: Learning-based approach for semantic image retrieval by using a dynamic semantic network. In: Proceedings of 19th International Conference on Database and Expert Systems Application, Italy, pp. 107–111 (2008) JingYBalujaSVisualRank: applying PageRamk to large-scale image searchIEEE Trans. Pattern Anal. Mach. Intell.2008301877189010.1109/TPAMI.2008.121 AlamdarFKeyvanpourMRA new color feature extraction method based on QuadHistogramProc. Environ. Sci.20111077778310.1016/j.proenv.2011.09.126 Gao, B., Liu, T.Y., Qin, T., Zheng, X., Cheng, Q., Ma, W.Y.: Web image clustering by consistent utilization of visual features and surrounding texts. In: Proceedings of the ACM multimedia, pp. 112–121 (2005) Li, Z., Xu, G., Li, M., W.Yi. Ma, Zhang, H.J.: Grouping WWW image search results by novel inhomogeneous clustering method. In: Proceedings of the 11th International Multimedia Modeling Conference, pp. 255–261 (2005) IttiLKochCNieburEA model of saliency-based visual attention for rapid scene analysisIEEE Trans. Pattern Anal. Mach. Intell.1998201254125910.1109/34.730558 MilaneseRGilSPunTAttentive mechanism for dynamic and static scene analysisOpt. Eng.1995342428243410.1117/12.205668 Cai, D., He, X., Li, Z., Ma, W.Y., Wen, J.R. : Hierarchical clustering of WWW image search results using visual, textual and link information. In: Proceedings of the ACM Multimedia, pp. 952–959 (2004) ManjunathBSOhmJVasudevanVVYamadaAColor and texture descriptorsIEEE Trans. Circuits Syst. Video Technol.20101170371510.1109/76.927424 Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., Ma, W.: IGroup: A Web Image Search Engine with Semantic Clustering of Search Results. In: Proceedings of the ACM, California, USA, pp. 377–384 (2006) Feng, H., Chua, T.S.: A bootstrapping approach to annotating large image collection. In: Proceedings of the ACM International Conference on Multimedia, Information Retrieval, pp 55–62 (2003) DaviesDLBouldinDWCluster separation measureIEEE Trans. Pattern Anal. Mach. Intell.1979195104 BS Manjunath (587_CR28) 2010; 11 M Halkidi (587_CR35) 2001; 17 587_CR10 587_CR32 587_CR11 B Duran (587_CR41) 1974 587_CR14 587_CR15 S Baluja (587_CR24) 1997; 22 587_CR37 587_CR16 G Milligan (587_CR36) 1985; 50 E Fowlkes (587_CR44) 1983; 78 H Tamura (587_CR30) 1978; 8 X Wang (587_CR33) 2004; 25 E Niebur (587_CR22) 1998 DL Davies (587_CR39) 1979; 1 F Alamdar (587_CR31) 2011; 10 E Guldogan (587_CR34) 2008; 2 L Itti (587_CR25) 1998; 20 587_CR20 D Lamming (587_CR21) 1991 J Boberg (587_CR38) 1993; 26 R Milanese (587_CR23) 1995; 34 587_CR26 587_CR27 W Williams (587_CR40) 1966; 54 Y Jing (587_CR42) 2008; 30 587_CR6 587_CR17 587_CR7 H Wang (587_CR12) 2009; 5803 587_CR18 587_CR19 J Wang (587_CR8) 2010; 17 587_CR9 587_CR2 587_CR3 587_CR4 587_CR5 X Tian (587_CR43) 2010; 19 M Meilă (587_CR45) 2007; 98 587_CR1 HT Shen (587_CR13) 2006; 11 H Eidenberger (587_CR29) 2004; 10 |
| References_xml | – reference: WangXWangYWangLImproving fuzzy c-means clustering based on feature-weight learningPattern Recogn. Lett.2004251123113210.1016/j.patrec.2004.03.008 – reference: Li, Z., Xu, G., Li, M., W.Yi. Ma, Zhang, H.J.: Grouping WWW image search results by novel inhomogeneous clustering method. In: Proceedings of the 11th International Multimedia Modeling Conference, pp. 255–261 (2005) – reference: LammingDCronly-DillonJContrast sensitivity Chapter 5Vision and Visual Dysfunction1991LondonMacmillan Press – reference: DaviesDLBouldinDWCluster separation measureIEEE Trans. Pattern Anal. Mach. Intell.1979195104 – reference: BalujaSPomerleauDAExpectation-based selective attention for visual monitoring and control of a robot vehicleRobot. Auton. Syst.19972232934410.1016/S0921-8890(97)00046-8 – reference: TamuraHMoriSYamawakiTTextural features corresponding visual perceptionIEEE Trans. Syst. Man Cybern.1978846047310.1109/TSMC.1978.4309999 – reference: AlamdarF.KeyvanpourM.R.Effects of quaghistogram on image retrieval and clusteringInt. J. Comput. Sci. Appl.20124141153 – reference: MilaneseRGilSPunTAttentive mechanism for dynamic and static scene analysisOpt. Eng.1995342428243410.1117/12.205668 – reference: ManjunathBSOhmJVasudevanVVYamadaAColor and texture descriptorsIEEE Trans. Circuits Syst. Video Technol.20101170371510.1109/76.927424 – reference: DuranBOdellPCluster Analysis: A Survey, Volume 100 of Lecture Notes in Economics and Mathematical Systems1974BerlinSpringer – reference: MeilăMComparing clusterings: an information based distanceJ. Multivar. Anal.20079887389510.1016/j.jmva.2006.11.01305170498 – reference: Cai, D., He, X., Li, Z., Ma, W.Y., Wen, J.R. : Hierarchical clustering of WWW image search results using visual, textual and link information. In: Proceedings of the ACM Multimedia, pp. 952–959 (2004) – reference: Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., Ma, W.: IGroup: A Web Image Search Engine with Semantic Clustering of Search Results. In: Proceedings of the ACM, California, USA, pp. 377–384 (2006) – reference: Liu, H., Xie, X., Tang, X., Li, Z., Ma, W.Y.: Effective browsing of web image search results. In: Proceedings of the Multimedia, Information Retrieval, pp. 84–90 (2004) – reference: Hirota, M., Yokoyama, S., Fukuta, N., Ishikawa, H.: Constraint-based clustering of image search results using photo metadata and low-level image features. In: Proceedings of the Computer and Information Science. Springer, Berlin, Heidelberg, pp. 165–178 (2010) – reference: NieburEKochCParasuramanRComputational architectures for attentionThe Attentive Brain1998Cambridge, MAMIT Press163186 – reference: FowlkesEMallowsCA method for comparing two hierarchical clusteringsJ. Am. Stat. Assoc.19837855356910.1080/01621459.1983.104780080545.62042 – reference: Jia, Y., Wang, J., Zhang, C., Hua, X.S.: Finding image exemplars using fast sparse affinity propagation. In: Proceedings of the ACM Multimedia, pp. 639–642 (2008) – reference: WangHMissuraOGärtnerTWrobelSContext-Based Clustering of Image Search ResultsLect. Notes Comput. Sci.2009580315316010.1007/978-3-642-04617-9_20 – reference: WangJJiaLHuaXSInteractive browsing via diversified visual summarization for image search resultsMultimed. Syst.20101737939110.1007/s00530-010-0224-7 – reference: Gao, B., Liu, T.Y., Qin, T., Zheng, X., Cheng, Q., Ma, W.Y.: Web image clustering by consistent utilization of visual features and surrounding texts. In: Proceedings of the ACM multimedia, pp. 112–121 (2005) – reference: Moëllic, P.A. , Haugeard, J.E. , Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: Proceedings of the CIVR, pp. 269–278 (2008) – reference: WilliamsWLambertJMultivariate methods in plant ecology: V. Similarity analyses and information-analysisJ. Ecol.19665442744510.2307/2257960 – reference: Asbaghi, Sh., Keyvanpour, M.R., Amiri, A.: Learning-based approach for semantic image retrieval by using a dynamic semantic network. In: Proceedings of 19th International Conference on Database and Expert Systems Application, Italy, pp. 107–111 (2008) – reference: Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from Google’s image search. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV), pp. 1816–1823 (2005) – reference: Feng, H., Chua, T.S.: A bootstrapping approach to annotating large image collection. In: Proceedings of the ACM International Conference on Multimedia, Information Retrieval, pp 55–62 (2003) – reference: JingYBalujaSVisualRank: applying PageRamk to large-scale image searchIEEE Trans. Pattern Anal. Mach. Intell.2008301877189010.1109/TPAMI.2008.121 – reference: van Leuken, R.H., Garcia, L., Olivares, X.: Visual Diversification of Image Search Result. In: Proceedings of the 18th International Conference on World Wide Web, ACM, USA, pp. 341–350 (2009) – reference: AlamdarFKeyvanpourMRA new color feature extraction method based on QuadHistogramProc. Environ. Sci.20111077778310.1016/j.proenv.2011.09.126 – reference: GuldoganEGabboujMFeature selection for content-based image retrievalSignal Image Video Process.20082324125010.1007/s11760-007-0049-91151.68717 – reference: IttiLKochCNieburEA model of saliency-based visual attention for rapid scene analysisIEEE Trans. Pattern Anal. Mach. Intell.1998201254125910.1109/34.730558 – reference: HalkidiMBaistakisYVazirgiannisMOn clustering validation techniquesJ. Intell. Inf. Syst.20011710714510.1023/A:10128016124830998.68154 – reference: Deselaers, T., Keysers, D., Ney, H.: Clustering Visually Similar Images to Improve Image Search Engines. In: Proceedings of the Informatiktage 2003 der Gesellschaft für Informatik, Germany (2003) – reference: van Zwol, R., Murdock, V., Garcia, L., Ramirez, G.: Diversifying image search with user generated content. In: Proceedings of the International ACM Conference on Multimedia Information Retrieval, Canada (2008) – reference: MilliganGCooperMAn examination of procedures for determining the number of clusters in a data setPsychometrika19855015917910.1007/BF02294245 – reference: BobergJSalakoskiTGeneral formulation and evaluation of agglomerative clustering methods with metric and non-metric distancesPattern Recogn.1993261395140610.1016/0031-3203(93)90145-M – reference: TianXTaoDHuaXSWuXActive reranking for web image searchIEEE Trans. Image Process.20101980582010.1109/TIP.2009.20358662756572 – reference: ShenHTTanKZhouXCuiBICICLE: a semantic-based retrieval system for WWW imagesMultimed. Syst.20061143845410.1007/s00530-006-0020-6 – reference: Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., Ma, W.Y.: Improving web search results using affinity graph. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, USA, pp. 504–511 (2005) – reference: Ma, Y., Zhang, H.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the Eleventh ACM International Conference on Multimedia, Berkeley, CA, USA (2003) – reference: EidenbergerHStatistical analysis of content-based MPEG-7 descriptors for image retrievalMultimed. Syst.200410849710.1007/s00530-004-0141-8 – reference: Alamdar, F., bahmani, Z., Haratizadeh, S.: Color quantization with clustering By F-PSO-GA. In: Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), China, pp. 233–238 (2010) – reference: HassanzadehH.KeyvanpourM.R.Semantic web requirements through web mining techniquesInt. J. Comput. Theory Eng.20124616620 – reference: Salvador, S., Chan, P.: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 576–584 (2004) – reference: Ahmad, S.: VISIT: a neural model of covert attention. Adv. Neural Inf. Process. Syst. 4, 420–427 (1991) – ident: 587_CR14 doi: 10.1145/1459359.1459448 – volume: 5803 start-page: 153 year: 2009 ident: 587_CR12 publication-title: Lect. Notes Comput. Sci. doi: 10.1007/978-3-642-04617-9_20 – ident: 587_CR37 doi: 10.1109/ICTAI.2004.50 – ident: 587_CR1 doi: 10.1145/1026711.1026726 – volume: 11 start-page: 703 year: 2010 ident: 587_CR28 publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/76.927424 – ident: 587_CR6 doi: 10.1145/1076034.1076120 – ident: 587_CR7 doi: 10.1109/ICCV.2005.142 – volume: 17 start-page: 379 year: 2010 ident: 587_CR8 publication-title: Multimed. Syst. doi: 10.1007/s00530-010-0224-7 – ident: 587_CR9 doi: 10.1145/1027527.1027747 – ident: 587_CR2 – ident: 587_CR26 – volume: 30 start-page: 1877 year: 2008 ident: 587_CR42 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.121 – ident: 587_CR15 doi: 10.1145/1101149.1101167 – volume: 25 start-page: 1123 year: 2004 ident: 587_CR33 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2004.03.008 – volume: 20 start-page: 1254 year: 1998 ident: 587_CR25 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.730558 – volume: 98 start-page: 873 year: 2007 ident: 587_CR45 publication-title: J. Multivar. Anal. doi: 10.1016/j.jmva.2006.11.013 – ident: 587_CR32 – ident: 587_CR4 doi: 10.1145/1526709.1526756 – ident: 587_CR20 doi: 10.1145/957013.957094 – volume: 54 start-page: 427 year: 1966 ident: 587_CR40 publication-title: J. Ecol. doi: 10.2307/2257960 – ident: 587_CR11 – ident: 587_CR17 doi: 10.1145/1386352.1386390 – volume: 10 start-page: 777 year: 2011 ident: 587_CR31 publication-title: Proc. Environ. Sci. doi: 10.1016/j.proenv.2011.09.126 – start-page: 163 volume-title: The Attentive Brain year: 1998 ident: 587_CR22 – volume: 34 start-page: 2428 year: 1995 ident: 587_CR23 publication-title: Opt. Eng. doi: 10.1117/12.205668 – ident: 587_CR19 doi: 10.1007/978-3-642-15405-8_14 – volume: 1 start-page: 95 year: 1979 ident: 587_CR39 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – ident: 587_CR3 doi: 10.1109/DEXA.2008.113 – volume: 8 start-page: 460 year: 1978 ident: 587_CR30 publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1978.4309999 – volume: 50 start-page: 159 year: 1985 ident: 587_CR36 publication-title: Psychometrika doi: 10.1007/BF02294245 – volume: 2 start-page: 241 issue: 3 year: 2008 ident: 587_CR34 publication-title: Signal Image Video Process. doi: 10.1007/s11760-007-0049-9 – volume: 22 start-page: 329 year: 1997 ident: 587_CR24 publication-title: Robot. Auton. Syst. doi: 10.1016/S0921-8890(97)00046-8 – ident: 587_CR18 doi: 10.1007/978-3-662-05300-3 – ident: 587_CR27 doi: 10.1109/ICICISYS.2010.5658548 – volume: 26 start-page: 1395 year: 1993 ident: 587_CR38 publication-title: Pattern Recogn. doi: 10.1016/0031-3203(93)90145-M – volume: 17 start-page: 107 year: 2001 ident: 587_CR35 publication-title: J. Intell. Inf. Syst. doi: 10.1023/A:1012801612483 – volume: 10 start-page: 84 year: 2004 ident: 587_CR29 publication-title: Multimed. Syst. doi: 10.1007/s00530-004-0141-8 – volume: 78 start-page: 553 year: 1983 ident: 587_CR44 publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1983.10478008 – ident: 587_CR10 doi: 10.1145/1180639.1180743 – volume: 11 start-page: 438 year: 2006 ident: 587_CR13 publication-title: Multimed. Syst. doi: 10.1007/s00530-006-0020-6 – volume-title: Cluster Analysis: A Survey, Volume 100 of Lecture Notes in Economics and Mathematical Systems year: 1974 ident: 587_CR41 – volume-title: Vision and Visual Dysfunction year: 1991 ident: 587_CR21 – ident: 587_CR5 doi: 10.1145/1460096.1460109 – ident: 587_CR16 – volume: 19 start-page: 805 year: 2010 ident: 587_CR43 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2009.2035866 |
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| Title | Effective browsing of image search results via diversified visual summarization by clustering and refining clusters |
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