Mostrar el registro sencillo del ítem

dc.contributor.authorSilva, Jesússpa
dc.contributor.authorVarela Izquierdo, Noelspa
dc.contributor.authorPineda, Omarspa
dc.date.accessioned2021-01-20T18:38:55Z
dc.date.available2021-01-20T18:38:55Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7729spa
dc.description.abstractContent-based image retrieval systems attempt to provide a means of searching for images in large repositories without using any information other than that contained in the image itself, usually in the form of low-level descriptors. Since these descriptors do not accurately represent the semantics of the image, evaluating the perceptual similarity between two images based only on them is not a trivial task. This paper describes an effective method for image recovery based on evolutionary computing techniques. The results are compared with those obtained by the classical approach of the movement of the query point and the rescheduling of the axes and by a technique based on self-organizing maps, showing a remarkably higher performance in the repositories.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceSmart Innovation, Systems and Technologiesspa
dc.subjectImage recoveryspa
dc.subjectIGAspa
dc.subjectGenetic algorithmspa
dc.titleEvolutionary algorithm for content-based image searchspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_20spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-4875-8_20spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1. Banuchitra, S., Kungumaraj, K.: A comprehensive survey of content based image retrieval techniques. Int. J. Eng. Comput. Sci. (IJECS), 5 (2016).spa
dc.relation.references2. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)spa
dc.relation.references3. Tunga, S., Jayadevappa, D., Gururaj, C.: A comparative study of content-based image retrieval trends and approaches. Int. J. Image Process. (IJIP) 9(3), 127 (2015)spa
dc.relation.references4. Tzelepi, M., Tefas, A.: Deep convolutional learning for content-based image retrieval. Neuro-Computing 275, 2467–2478 (2018)spa
dc.relation.references5. Chen, L., Zhang, Y., Song, Z.L., Miao, Z.: Automatic web services classification based on rough set theory. J. Central South Univ. 20, 2708–2714 (2013)spa
dc.relation.references6. Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innov: J. Sci. Technol. 5(2), 61–75 (2017)spa
dc.relation.references7. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010)spa
dc.relation.references8. Thames L., Schaefer, D.: Softwaredefined cloud manufacturing for industry 4.0. In: Procedía CIRP, vol. 52, pp. 12–17 (2016)spa
dc.relation.references9. Viloria A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40, pp. 1249–1254 (2019a)spa
dc.relation.references10. Viloria, A., Pineda Lezama, O.B.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019b)spa
dc.relation.references11. Nisa, R., Qamar, U.: A text mining-based approach for web service classification. In: Information Systems and e-Business Management, pp. 1–18 (2014)spa
dc.relation.references12. Wu, J., Chen, L., Zheng, Z., Lyu, M.R., Wu, Z.: Clustering web services to facilitate service discovery. Knowl. Inf. Syst. 38(1), 207–229 (2014)spa
dc.relation.references13. Paulin M., et al.: Convolutional patch representations for image retrieval: an unsupervised approach. Int. J. Comput. Vis. 165–166 (2017)spa
dc.relation.references14. Chandrasekhar, V., Lin, J., Liao, Q., Morere, O., Veillard, A., Duan, L., Poggio, T.: Compression of deep neural networks for image instance retrieval. arXiv:1701.04923 (2017)spa
dc.relation.references15. Sharif, U., Mehmood, Z., Mahmood, T., Javid, M.A., Rehman, A., Saba, T.: Scene analysis and search using local features and support vector machine for effective content-based image retrieval. Artif. Intell. Rev. 52(2), 901–925 (2019)spa
dc.relation.references16. Korytkowski, M., Senkerik, R., Scherer, M.M., Angryk, R.A., Kordos, M., Siwocha, A.: Efficient image retrieval by fuzzy rules from boosting and metaheuristic. J. Artif. Intell. Soft Comput. Res. 10(1), 57–69 (2020)spa
dc.relation.references17. Abdi, Y., Feizi-Derakhshi, M.R.: Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems. Appl. Soft Comput. 87, 105991 (2020)spa
dc.relation.references18. Sarkar, S., Das, S., Chaudhuri, S.S.: Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl. Soft Comput. 50, 142–157 (2017)spa
dc.relation.references19. de Ves, E., Domingo, J., Ayala, G., Zuccarello, P.: A novel bayesian framework for relevance feedback in image content-based retrieval systems. Pattern Recogn. 39, 1622–1632 (2006)spa
dc.relation.references20. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS peer-to-peer). Ind. J. Sci. Technol. 9, 46spa
dc.relation.references21. Koskela, M., Laaksonen, J., & Oja E.: (2004) Use of image subset features in image retrieval with self-organizing maps. In: Image and Video Retrieval: Third International Conference, Dublin, Ireland, July 2004, pp. 508–516spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


Ficheros en el ítem

Thumbnail
Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos científicos [3120]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International