By Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)

ISBN-10: 3540221638

ISBN-13: 9783540221630

ISBN-10: 3540259813

ISBN-13: 9783540259817

This booklet is a longer choice of contributions that wereoriginally subm- ted to the first foreign Workshop on Adaptive Multimedia Retrieval (AMR 2003), which used to be geared up as a part of the twenty sixth German convention on Arti?cial Intelligence (KI 2003),and held in the course of September 15–18,2003at the college of Hamburg, Germany. prompted by way of the general luck of the workshop – as published via the stimulating surroundings in the course of the workshop and the variety of very and energetic members – we ?nally determined to edit a booklet according to revised papers that have been at the beginning submitted to the workshop. in addition, we invited a few extra introductory contributions with the intention to have the capacity to offer a conclusive booklet on present issues within the region of adaptive multimedia retrieval structures. we are hoping that we have been capable of prepare a stimulating selection of articles for the reader. we love to thank the organizationcommittee of the twenty sixth German convention on Arti?cial Intelligence (KI 2003) for offering the atmosphere and the admin- trative help in figuring out this workshop as a part of their application. specifically, we love to thank Christopher Habel for selling the workshop as a part of the convention software and Andreas Gun ¨ ther for his style help through the association process.

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Additional info for Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers

Example text

Using this strategy, integrated with the vector space model for retrieval, the notion of similarity is interactively adapted to the user profile by distorting components of the indexing space. The Viper system [5] adapts classical document retrieval techniques to the context of CBIR. It includes Rocchio’s algorithm as a way to handle feedback and to account for the sparsity of positive examples against negative examples. By tuning and parameters in Equation (1), this system is robust against abundant negative examples that would normally make retrieval inconsistent [4].

In: 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’00), Vancouver, British Columbia, Canada (2000) 258 10. : Learning from user feedback in image retrieval systems. : Advances in Neural Information Processing Systems 12, MIT Press (2000) 11. : PicHunter: Theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing 9 (2000) 20–37 12. : Support vector machine active learning for image retrieval. In: ACM Multimedia 2001, Ottawa, Ontario, Canada (2001) 107–118 13.

Having established such a clustering, the method then processes a query image with unknown annotation by subdividing it into segments and predicting its likely keywords from those of the clusters to which the query image segments are most similar. A two-dimensional multiresolution hidden Markov model (2D MHMM) [34] is at the core of another statistical approach to automatic semantic augmentation proposed by Li and Wang [35]. The author’s system for automatic linguistic indexing of pictures (ALIP) operates with a predefined number of semantic image categories, specified by sets of keywords according to the problem domain.

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Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers by Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)

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