By Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)
This publication is a longer number of contributions that wereoriginally subm- ted to the first overseas Workshop on Adaptive Multimedia Retrieval (AMR 2003), which was once prepared 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 collage of Hamburg, Germany. inspired by means of the general good fortune of the workshop – as published via the stimulating surroundings through the workshop and the variety of very and energetic individuals – we ?nally made up our minds to edit a booklet according to revised papers that have been in the beginning submitted to the workshop. in addition, we invited a few extra introductory contributions as a way to have the ability to supply a conclusive ebook on present issues within the zone of adaptive multimedia retrieval platforms. we are hoping that we have been in a position to prepare a stimulating choice of articles for the reader. we love to thank the organizationcommittee of the twenty sixth German convention on Arti?cial Intelligence (KI 2003) for supplying the environment and the admin- trative help in figuring out this workshop as a part of their application. specifically, we adore to thank Christopher Habel for selling the workshop as a part of the convention application and Andreas Gun ¨ ther for his style help during the association process.
Read Online or Download Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers PDF
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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
According to the adopted translation model, the authors consider the problem of object recognition as the one of machine translation. Given a representation in one form (image regions, or blobs, derived by clustering segmented images) they attempt to turn it into another form (keywords) using a developed model that acts in the capacity of a lexicon. Thus, the pairs of images and their respective annotation keyword sets are regarded as aligned bitexts, in which word-to-blob correspondence is to be established (see Figure 4 for an example).
For example, the teacher may take into account factors such as the aspects they plan to assess in final examinations, what is taught in prerequisite or concurrent courses that students undertake as well as the demands of subsequent courses. In this chapter, we describe our exploration of ways to repurpose our user model visualization tool to support creation of metadata on learning objects so that student interactions with these can serve as a basis for creating evidence about a user’s developing knowledge.
Typical scenario of retrieval with relevance feedback relevance judgments supplied by the user via an additive adjustment formula known as Rocchio’s algorithm: where is a query feature vector from the previous relevance feedback iteration, and are given documents from relevant and non-relevant sets, respectively, and and are tuning parameters. 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.