@inbook{rocchio-relevance-1971,
        title = {Relevance feedback in information retrieval. In: The SMART Retrieval System ­ Experiments in Automatic Document Processing.},
        address = {Englewood Cliffs, N.J.: Prentice Hall},
        author = { Rocchio},
        editor = {G. Salton},
        pages = {313-323},
        year = 1971,
        biburl = {http://www.bibsonomy.org/bibtex/2097695b2df3f8f5b444860bb9a398260/robo},
	keywords = {feedback information document relevance processing retrieval wismasys0809 smart}
}

@inproceedings{buckley94effect,
        title = {The effect of adding relevance information in a relevance feedback environment},
        author = {C. Buckley and G. Salton and J. Allan},
        booktitle = {Proceedings of the seventeenth annual international {ACM}-{SIGIR} conference on research and development in information retrieval},
        publisher = {Springer-Verlag},
        year = 1994,
        url = {citeseer.ist.psu.edu/buckley94effect.html},
        biburl = {http://www.bibsonomy.org/bibtex/2f157419cdcae5333b2a36f2d3dea64c7/jil},
	keywords = {feedback gewichte weights rocchio relevance gewichtung}
}

@article{974906,
        title = {A survey on the use of relevance feedback for information access systems},
        address = {New York, NY, USA},
        author = {Ian Ruthven and Mounia Lalmas},
        journal = {Knowl. Eng. Rev.},
        number = 2,
        pages = {95--145},
        publisher = {Cambridge University Press},
        volume = 18,
        year = 2003,
        url = {http://personal.cis.strath.ac.uk/~ir/papers/ker.pdf},
        issn = {0269-8889},
	doi = {http://dx.doi.org/10.1017/S0269888903000638},
	description = {A survey on the use of relevance feedback for information access systems},
	abstract = {Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems.},
	biburl = {http://www.bibsonomy.org/bibtex/20357d6b4d3aa885a0978036d50136373/hotho},
	keywords = {feedback ir expansion surevy query relevance}
}

@inproceedings{rui:ieee1997,
        title = {A Relevance Feedback Architecture for Content-Based Multimedia Information
	Retrieval Systems},
        annote = {Paper + PDF},
        author = {Y. Rui and T. S. Huang and S. Mehrotra and M. Ortega},
        booktitle = {Proceedings of IEEE Workshop on Content-based Access of Image and
	Video Libraries},
        pages = {82--89},
        year = 1997,
        description = {Dissertation},
	biburl = {http://www.bibsonomy.org/bibtex/278fac7585a192f63826f8799ea7c1d26/lm77},
	keywords = {Relevance Feedback}
}

@inproceedings{rui:icip1997,
        title = {Content-Based Image Retrieval with Relevance Feedback in {MARS}},
        annote = {Paper + PDF},
        author = {Y. Rui and T. S. Huang and S. Mehrotra},
        booktitle = {Proceedings of IEEE Int. Conf. Image Processing},
        pages = {815--818},
        year = 1997,
        description = {Dissertation},
	biburl = {http://www.bibsonomy.org/bibtex/29ddb9ec329b1c1b6f2e34ae4e97dd797/lm77},
	keywords = {Relevance Feedback}
}

@mastersthesis{minka:msc,
        title = {An Image Database Browser that Learns from User Interaction},
        annote = {Paper + PDF},
        author = {T. Minka},
        school = {MIT},
        type = {M{E}ng Thesis},
        year = 1996,
        description = {Dissertation},
	biburl = {http://www.bibsonomy.org/bibtex/20277fdf8b99427a4eb9c5f52efd46e0b/lm77},
	keywords = {Relevance Feedback}
}

@article{gevers:ieee2000,
        title = {Pic{T}o{S}eek: combining color and shape invariant features for image
	retrieval},
        annote = {Paper + PDF},
        author = {T. Gevers and A.W.M. Smeulders},
        journal = {IEEE Transactions on Image Processing},
        month = {January},
        number = 1,
        pages = {102--119},
        volume = 9,
        year = 2000,
        description = {Dissertation},
	abstract = {We aim at combining color and shape invariants for indexing and retrieving
	images. To this end, color models are proposed independent of the
	object geometry, object pose, and illumination. From these color
	models, color invariant edges are derived from which shape invariant
	features are computed. Computational methods are described to combine
	the color and shape invariants into a unified high-dimensional invariant
	feature set for discriminatory object retrieval. Experiments have
	been conducted on a database consisting of 500 images taken from
	multicolored man-made objects in real world scenes. From the theoretical
	and experimental results it is concluded that object retrieval based
	on composite color and shape invariant features provides excellent
	retrieval accuracy. Object retrieval based on color invariants provides
	very high retrieval accuracy whereas object retrieval based entirely
	on shape invariants yields poor discriminative power. Furthermore,
	the image retrieval scheme is highly robust to partial occlusion,
	object clutter and a change in the object's pose. Finally, the image
	retrieval scheme is integrated into the PicToSeek system on-line
	at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching
	images on the World Wide Web.},
	biburl = {http://www.bibsonomy.org/bibtex/23b2394c8268479eba4d53a385f8612a4/lm77},
	keywords = {PicToSeek, Relevance Implementation, Feedback}
}

@inproceedings{cox:icpr1996,
        title = {PicHunter: Bayesian Relevance Feedback for Image Retrieval},
        address = {Vienna, Austria},
        author = {I. J. Cox and M. L. Miller and S. M. Omohundro and P.N. Yianilos},
        booktitle = {Proceedings of the International Conference on Pattern Recognition},
        year = 1996,
        description = {Dissertation},
	biburl = {http://www.bibsonomy.org/bibtex/2c276d25a0dd0a716ecbfdebc78b79871/lm77},
	keywords = {Bayesian, Relevance Implementation, Feedback PicHunter,}
}

@inproceedings{cox:adl1996,
        title = {Target Testing and the {P}ic{H}unter {B}ayesian Multimedia Retrieval
	System},
        address = {Washington D.C.},
        author = {I. J. Cox and M. L. Miller and S. M. Omohundro and P.N. Yianilos},
        booktitle = {Advanced Digital Libraries Forum},
        year = 1996,
        description = {Dissertation},
	biburl = {http://www.bibsonomy.org/bibtex/2b23b482836551175c80096a3fba2a749/lm77},
	keywords = {Bayesian, Relevance Implementation, Feedback PicHunter,}
}

@article{cox:ieee2000,
        title = {The {B}ayesian image retrieval system, {P}ic{H}unter: theory, implementation,
	and psychophysical experiments},
        annote = {Paper + PDF},
        author = {I.J. Cox and M.L. Miller and T.P. Minka and T.V. Papathomas and P.N. Yianilos},
        journal = {IEEE Transactions on Image Processing},
        month = {January},
        number = 1,
        pages = {20--37},
        volume = 9,
        year = 2000,
        description = {Dissertation},
	abstract = {Presents the theory, design principles, implementation and performance
	results of PicHunter, a prototype content-based image retrieval (CBIR)
	system. In addition, this document presents the rationale, design
	and results of psychophysical experiments that were conducted to
	address some key issues that arose during PicHunter's development.
	The PicHunter project makes four primary contributions to research
	on CBIR. First, PicHunter represents a simple instance of a general
	Bayesian framework which we describe for using relevance feedback
	to direct a search. With an explicit model of what users would do,
	given the target image they want, PicHunter uses Bayes's rule to
	predict the target they want, given their actions. This is done via
	a probability distribution over possible image targets, rather than
	by refining a query. Second, an entropy-minimizing display algorithm
	is described that attempts to maximize the information obtained from
	a user at each iteration of the search. Third, PicHunter makes use
	of hidden annotation rather than a possibly inaccurate/inconsistent
	annotation structure that the user must learn and make queries in.
	Finally, PicHunter introduces two experimental paradigms to quantitatively
	evaluate the performance of the system, and psychophysical experiments
	are presented that support the theoretical claims.},
	biburl = {http://www.bibsonomy.org/bibtex/208c771b5340d508cb4bcfc07cd9db9a8/lm77},
	keywords = {Relevance Implementation, Feedback PicHunter,}
}