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  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2010 TO 2020}
  1. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.00 
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    Abstract
    Pseudorelevance feedback (PRF) was proposed to solve the limitation of relevance feedback (RF), which is based on the user-in-the-loop process. In PRF, the top-k retrieved images are regarded as PRF. Although the PRF set contains noise, PRF has proven effective for automatically improving the overall retrieval result. To implement PRF, the Rocchio algorithm has been considered as a reasonable and well-established baseline. However, the performance of Rocchio-based PRF is subject to various representation choices (or factors). In this article, we examine these factors that affect the performance of Rocchio-based PRF, including image-feature representation, the number of top-ranked images, the weighting parameters of Rocchio, and similarity measure. We offer practical insights on how to optimize the performance of Rocchio-based PRF by choosing appropriate representation choices. Our extensive experiments on NUS-WIDE-LITE and Caltech 101 + Corel 5000 data sets show that the optimal feature representation is color moment + wavelet texture in terms of retrieval efficiency and effectiveness. Other representation choices are that using top-20 ranked images as pseudopositive and pseudonegative feedback sets with the equal weight (i.e., 0.5) by the correlation and cosine distance functions can produce the optimal retrieval result.
    Language
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  2. Costa Carvalho, A. da; Rossi, C.; Moura, E.S. de; Silva, A.S. da; Fernandes, D.: LePrEF: Learn to precompute evidence fusion for efficient query evaluation (2012) 0.00 
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    Date
    24. 6.2012 14:29:10
    Language
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  3. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.00 
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    Date
    29. 9.2018 12:32:59
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  4. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.00 
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    Date
    11. 6.2012 14:22:34
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  5. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.00 
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    Date
    22. 3.2013 19:34:49
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  6. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018) 0.00 
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    Date
    29. 9.2018 11:43:31
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  7. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.00 
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  8. He, J.; Meij, E.; Rijke, M. de: Result diversification based on query-specific cluster ranking (2011) 0.00 
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  9. Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015) 0.00 
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  10. Abdelkareem, M.A.A.: In terms of publication index, what indicator is the best for researchers indexing, Google Scholar, Scopus, Clarivate or others? (2018) 0.00 
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  11. Fu, X.: Towards a model of implicit feedback for Web search (2010) 0.00 
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  12. Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.00 
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  13. Cecchini, R.L.; Lorenzetti, C.M.; Maguitman, A.G.; Brignole, N.B.: Multiobjective evolutionary algorithms for context-based search (2010) 0.00 
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  14. Efron, M.: Linear time series models for term weighting in information retrieval (2010) 0.00 
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  15. Ding, Y.: Topic-based PageRank on author cocitation networks (2011) 0.00 
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  16. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.00 
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  17. Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012) 0.00 
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  18. Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012) 0.00 
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  19. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.00 
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  20. Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.00 
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