Evolutionary algorithm for content-based image search
Content-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...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7729
- Acceso en línea:
- https://hdl.handle.net/11323/7729
https://doi.org/10.1007/978-981-15-4875-8_20
https://repositorio.cuc.edu.co/
- Palabra clave:
- Image recovery
IGA
Genetic algorithm
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Evolutionary algorithm for content-based image search |
| title |
Evolutionary algorithm for content-based image search |
| spellingShingle |
Evolutionary algorithm for content-based image search Image recovery IGA Genetic algorithm |
| title_short |
Evolutionary algorithm for content-based image search |
| title_full |
Evolutionary algorithm for content-based image search |
| title_fullStr |
Evolutionary algorithm for content-based image search |
| title_full_unstemmed |
Evolutionary algorithm for content-based image search |
| title_sort |
Evolutionary algorithm for content-based image search |
| dc.creator.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Image recovery IGA Genetic algorithm |
| topic |
Image recovery IGA Genetic algorithm |
| description |
Content-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. |
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2020 |
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2020 |
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2021-01-20T18:38:55Z |
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2021-01-20T18:38:55Z |
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http://purl.org/coar/resource_type/c_6501 |
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https://doi.org/10.1007/978-981-15-4875-8_20 |
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Corporación Universidad de la Costa |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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1. Banuchitra, S., Kungumaraj, K.: A comprehensive survey of content based image retrieval techniques. Int. J. Eng. Comput. Sci. (IJECS), 5 (2016). 2. 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) 3. 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) 4. Tzelepi, M., Tefas, A.: Deep convolutional learning for content-based image retrieval. Neuro-Computing 275, 2467–2478 (2018) 5. 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) 6. 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) 7. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010) 8. Thames L., Schaefer, D.: Softwaredefined cloud manufacturing for industry 4.0. In: Procedía CIRP, vol. 52, pp. 12–17 (2016) 9. Viloria A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40, pp. 1249–1254 (2019a) 10. 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) 11. Nisa, R., Qamar, U.: A text mining-based approach for web service classification. In: Information Systems and e-Business Management, pp. 1–18 (2014) 12. 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) 13. Paulin M., et al.: Convolutional patch representations for image retrieval: an unsupervised approach. Int. J. Comput. Vis. 165–166 (2017) 14. 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) 15. 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) 16. 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) 17. 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) 18. 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) 19. 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) 20. 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, 46 21. 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. 508–516 |
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Silva, JesúsVarela Izquierdo, NoelPineda, Omar2021-01-20T18:38:55Z2021-01-20T18:38:55Z2020https://hdl.handle.net/11323/7729https://doi.org/10.1007/978-981-15-4875-8_20Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Content-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.Silva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_20Image recoveryIGAGenetic algorithmEvolutionary algorithm for content-based image searchArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Banuchitra, S., Kungumaraj, K.: A comprehensive survey of content based image retrieval techniques. Int. J. Eng. Comput. Sci. (IJECS), 5 (2016).2. 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)3. 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)4. Tzelepi, M., Tefas, A.: Deep convolutional learning for content-based image retrieval. Neuro-Computing 275, 2467–2478 (2018)5. 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)6. 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)7. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010)8. Thames L., Schaefer, D.: Softwaredefined cloud manufacturing for industry 4.0. In: Procedía CIRP, vol. 52, pp. 12–17 (2016)9. Viloria A., Neira-Rodado, D., Pineda Lezama, O.B.: Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40, pp. 1249–1254 (2019a)10. 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)11. Nisa, R., Qamar, U.: A text mining-based approach for web service classification. In: Information Systems and e-Business Management, pp. 1–18 (2014)12. 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)13. Paulin M., et al.: Convolutional patch representations for image retrieval: an unsupervised approach. Int. J. Comput. Vis. 165–166 (2017)14. 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)15. 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)16. 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)17. 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)18. 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)19. 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)20. 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, 4621. Koskela, M., Laaksonen, J., & Oja E.: (2004) Use of image subset features in image retrieval with self-organizing maps. 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