Show simple item record

dc.creatorSilva, Jesús
dc.creatorVarela Izquierdo, Noel
dc.creatorPineda, Omar
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourceSmart Innovation, Systems and Technologiesspa
dc.subjectImage recoveryspa
dc.subjectGenetic algorithmspa
dc.titleEvolutionary algorithm for content-based image searchspa
dcterms.references1. Banuchitra, S., Kungumaraj, K.: A comprehensive survey of content based image retrieval techniques. Int. J. Eng. Comput. Sci. (IJECS), 5 (2016).spa
dcterms.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
dcterms.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
dcterms.references4. Tzelepi, M., Tefas, A.: Deep convolutional learning for content-based image retrieval. Neuro-Computing 275, 2467–2478 (2018)spa
dcterms.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
dcterms.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
dcterms.references7. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010)spa
dcterms.references8. Thames L., Schaefer, D.: Softwaredefined cloud manufacturing for industry 4.0. In: Procedía CIRP, vol. 52, pp. 12–17 (2016)spa
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.references13. Paulin M., et al.: Convolutional patch representations for image retrieval: an unsupervised approach. Int. J. Comput. Vis. 165–166 (2017)spa
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International