PicHunter: Bayesian relevance feedback for image retrieval. This paper describes PicHunter, an image retrieval system that implements a novel approach to relevance feedback, such that the entire history of user selections contributes to the system’s estimate of the user’s goal image. To accomplish this, PicHunter uses Bayesian learning based on a probabilistic model of a user’s behavior. The predictions of this model are combined with the selections made during a search to estimate the probability associated with each image. These probabilities are then used to select images for display. Details of our model of a user’s behavior were tuned using an off-line leaning algorithm. For clarity, our studies were done with the simplest possible user interface but the algorithm can easily be incorporated into systems which support complex queries, including most previously proposed systems. However, even with this constraint and simple image features, PicHunter is able to locate randomly selected targets in a database of 4522 images after displaying an average of only 55 groups of 4 images which is over 10 times better than chance. We therefore expect that the performance of current image database retrieval systems can be improved by incorporation of the techniques described here.

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  1. Branson, Steve; Van Horn, Grant; Wah, Catherine; Perona, Pietro; Belongie, Serge: The ignorant led by the blind: a hybrid human-machine vision system for fine-grained categorization (2014)
  2. Ajorloo, Hossein; Lakdashti, Abolfazl: HBIR: hypercube-based image retrieval (2012)
  3. Ciocca, Gianluigi; Cusano, Claudio; Santini, Simone; Schettini, Raimondo: Halfway through the semantic gap: prosemantic features for image retrieval (2011)
  4. dos Santos, J.A.; Ferreira, C.D.; da S.Torres, R.; Gonçalves, M.A.; Lamparelli, R.A.C.: A relevance feedback method based on genetic programming for classification of remote sensing images (2011)
  5. Liu, Qingshan; Huang, Yuchi; Metaxas, Dimitris N.: Hypergraph with sampling for image retrieval (2011)
  6. Arevalillo-Herráez, Miguel; Ferri, Francesc J.; Domingo, Juan: A naive relevance feedback model for content-based image retrieval using multiple similarity measures (2010)
  7. Durán, M.Luisa; Rodríguez, Pablo G.; Arias-Nicolás, J.Pablo; Martín, Jacinto; Disdier, Carlos: A perceptual similarity method by pairwise comparison in a medical image case (2010)
  8. Yap, Kim-Hui; Wu, Kui; Zhu, Ce: Knowledge propagation in collaborative tagging for image retrieval (2010)
  9. Zhang, Jun; Ye, Lei: Series feature aggregation for content-based image retrieval (2010)
  10. Ji, Rongrong; Yao, Hongxun; Xu, Pengfei; Sun, Xiaoshuai: Visual and textual fusion for semantically supervised region-based retrieval (2009)
  11. Shen, Jialie: Stochastic modeling western paintings for effective classification (2009)
  12. Ferecatu, Marin; Boujemaa, Nozha; Crucianu, Michel: Semantic interactive image retrieval combining visual and conceptual content description (2008)
  13. Lee, Robert S.; Chung, Chin-Wan; Lee, Seok-Lyong; Kim, Sang-Hee: Confidence interval approach to feature re-weighting (2008)
  14. Liu, Danzhou; Hua, Kien A.; Yu, Ning: Efficiently support concurrent queries in multiuser CBIR systems (2008)
  15. Liu, Rujie; Wang, Yuehong; Baba, Takayuki; Masumoto, Daiki; Nagata, Shigemi: SVM-based active feedback in image retrieval using clustering and unlabeled data (2008)
  16. Franco, Annalisa; Lumini, Alessandra: Mixture of KL subspaces for relevance feedback (2007)
  17. León, T.; Zuccarello, P.; Ayala, G.; de Ves, E.; Domingo, J.: Applying logistic regression to relevance feedback in image retrieval systems (2007)
  18. Mani, Ankur; Sundaram, Hari: Modeling user context with applications to media retrieval (2007)
  19. Oyekoya, Oyewole; Stentiford, Fred: Perceptual image retrieval using eye movements (2007)
  20. Wu, Kui; Yap, Kim-Hui: Content-based image retrieval using fuzzy perceptual feedback (2007)

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