Agregação Não Supervisionada de Rankings para Redução de Cold-Start em Recuperação Multimodal de Imagens
Resumo
Em sistemas de recuperação de imagens por conteúdo, a técnica de realimentação de relevância visa permitir ao usuário exprimir a sua necessidade de busca sem conhecer propriedades de baixo nível das imagens. Para o bom funcionamento desta técnica, o conjunto de resultados anterior à primeira interação do usuário deve apresentar resultados relevantes. Para atenuar o problema de cold-start e aprimorar o conjunto inicial de resultados, neste trabalho realizou-se uma avaliação experimental de métodos de agregação de rankings para combinar resultados obtidos a partir de diferentes critérios de ranqueamento de imagens. Resultados considerando diferentes modalidades de características indicam eficácia promissora em relação aos baselines.
Palavras-chave:
Rank Aggregation, Cold-Start, Image Retrieval
Referências
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Atrey, P. K., Hossain, M. A., El Saddik, A., and Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Syst., 16(6):345–379. DOI: https://doi.org/10.1007/s00530-010-0182-0
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Calumby, R., R. da S, T., and M. A, G. (2014). Multimodal retrieval with relevance feedback based on genetic programming. MTAP, (69):991–1019. DOI: https://doi.org/10.1007/s11042-012-1152-7
Calumby, R. T., Gonc¸alves, M. A., and da Silva Torres, R. (2017). Diversity-based interactive learning meets multimodality. Neurocomputing, 259:159 – 175. DOI: https://doi.org/10.13039/501100002322
Cormack, G. V., Clarke, C. L. A., and Buettcher, S. (2009). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of the 32Nd SIGIR, pages 758–759. ACM. DOI: https://doi.org/10.1145/1571941.1572114
dos Santos, K. C. L., de Almeida, H. M., Gonçalves, M. A., and da Silva Torres, R. (2009). Recuperacão de imagens da web utilizando múltiplas evidências textuais e programação genética. In Proceedings of the XXIV SBBD, pages 91–105. DOI: https://doi.org/
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Efficient similarity search and classification via rank aggregation. In Proceedings of the SIGMOD, pages 301–312. ACM. DOI: https://doi.org/10.1145/872757.872795
Ferreira, C., Santos, J., da S. Torres, R., Gonçalves, M., Rezende, R., and Fan, W. (2011). Relevance feedback based on genetic programming for image retrieval. Pattern Recognition Letters, 32(1):27 – 37. DOI: https://doi.org/10.1016/j.patrec.2010.05.015
Lewis, J., Ossowski, S., Hicks, J., Errami, M., and Garner, H. R. (2006). Text similarity: an alternative way to search MEDLINE. Bioinformatics, 22(18):2298–2304. DOI: https://doi.org/10.1093/bioinformatics/btl388
Lin, S. (2010). Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570. DOI: https://doi.org/10.1002/wics.111
Mei, T., Rui, Y., Li, S., and Tian, Q. (2014). Multimedia search reranking: A literature survey. ACM Comput. Surv., 46(3):38:1–38:38. DOI: https://doi.org/10.1145/2536798
Penatti, O. A., Valle, E., and da S. Torres, R. (2012). Comparative study of global color and texture descriptors for web image retrieval. Journal of Visual Communication and Image Representation, 23(2):359 – 380. DOI: https://doi.org/10.1016/j.jvcir.2011.11.002
Shaw, J. A. and Fox, E. A. (1994). Combination of multiple searches. In TREC-2, pages 243–252. DOI: https://doi.org/
Torres, R. D. S. and Falcão, A. X. (2006). Content-based image retrieval: Theory and applications. Revista de Informática Teórica e Aplicada, 13:161–185. DOI: https://doi.org/
Vargas Muñoz, J. A., da Silva Torres, R., and Gonçalves, M. A. (2015). A soft computing approach for learning to aggregate rankings. In Proceedings of the 24th CIKM, pages 83–92. ACM. DOI: https://doi.org/10.1145/2806416.2806478
Young, H. (1974). An axiomatization of borda’s rule. Journal of Economic Theory, 9(1):43 – 52. DOI: https://doi.org/
Atrey, P. K., Hossain, M. A., El Saddik, A., and Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Syst., 16(6):345–379. DOI: https://doi.org/10.1007/s00530-010-0182-0
Baeza-Yates, R. and Ribeiro-Neto, B. (2008). Modern Information Retrieval: The Concepts and Technology Behind Search. USA, 2nd edition. DOI: https://doi.org/10.5860/choice.48-6950
Calumby, R., R. da S, T., and M. A, G. (2014). Multimodal retrieval with relevance feedback based on genetic programming. MTAP, (69):991–1019. DOI: https://doi.org/10.1007/s11042-012-1152-7
Calumby, R. T., Gonc¸alves, M. A., and da Silva Torres, R. (2017). Diversity-based interactive learning meets multimodality. Neurocomputing, 259:159 – 175. DOI: https://doi.org/10.13039/501100002322
Cormack, G. V., Clarke, C. L. A., and Buettcher, S. (2009). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of the 32Nd SIGIR, pages 758–759. ACM. DOI: https://doi.org/10.1145/1571941.1572114
dos Santos, K. C. L., de Almeida, H. M., Gonçalves, M. A., and da Silva Torres, R. (2009). Recuperacão de imagens da web utilizando múltiplas evidências textuais e programação genética. In Proceedings of the XXIV SBBD, pages 91–105. DOI: https://doi.org/
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Efficient similarity search and classification via rank aggregation. In Proceedings of the SIGMOD, pages 301–312. ACM. DOI: https://doi.org/10.1145/872757.872795
Ferreira, C., Santos, J., da S. Torres, R., Gonçalves, M., Rezende, R., and Fan, W. (2011). Relevance feedback based on genetic programming for image retrieval. Pattern Recognition Letters, 32(1):27 – 37. DOI: https://doi.org/10.1016/j.patrec.2010.05.015
Lewis, J., Ossowski, S., Hicks, J., Errami, M., and Garner, H. R. (2006). Text similarity: an alternative way to search MEDLINE. Bioinformatics, 22(18):2298–2304. DOI: https://doi.org/10.1093/bioinformatics/btl388
Lin, S. (2010). Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570. DOI: https://doi.org/10.1002/wics.111
Mei, T., Rui, Y., Li, S., and Tian, Q. (2014). Multimedia search reranking: A literature survey. ACM Comput. Surv., 46(3):38:1–38:38. DOI: https://doi.org/10.1145/2536798
Penatti, O. A., Valle, E., and da S. Torres, R. (2012). Comparative study of global color and texture descriptors for web image retrieval. Journal of Visual Communication and Image Representation, 23(2):359 – 380. DOI: https://doi.org/10.1016/j.jvcir.2011.11.002
Shaw, J. A. and Fox, E. A. (1994). Combination of multiple searches. In TREC-2, pages 243–252. DOI: https://doi.org/
Torres, R. D. S. and Falcão, A. X. (2006). Content-based image retrieval: Theory and applications. Revista de Informática Teórica e Aplicada, 13:161–185. DOI: https://doi.org/
Vargas Muñoz, J. A., da Silva Torres, R., and Gonçalves, M. A. (2015). A soft computing approach for learning to aggregate rankings. In Proceedings of the 24th CIKM, pages 83–92. ACM. DOI: https://doi.org/10.1145/2806416.2806478
Young, H. (1974). An axiomatization of borda’s rule. Journal of Economic Theory, 9(1):43 – 52. DOI: https://doi.org/
Publicado
07/10/2019
Como Citar
DA SILVA, Wanderson Bezerra; CALUMBY, Rodrigo Tripodi.
Agregação Não Supervisionada de Rankings para Redução de Cold-Start em Recuperação Multimodal de Imagens. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 277-282.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8836.
