By Thomas Sødring, Alan F. Smeaton (auth.), Fabio Crestani, Mark Girolami, Cornelis Joost van Rijsbergen (eds.)
The annual colloquium on details retrieval examine presents a chance for either new and validated researchers to give papers describing paintings in growth or ?nal effects. This colloquium was once verified through the BCS IRSG(B- tish laptop Society details Retrieval expert Group), and named the once a year Colloquium on details Retrieval learn. lately, the positioning of the colloquium has alternated among the uk and continental Europe. To re?ect the growing to be ecu orientation of the development, the colloquium used to be renamed “European Annual Colloquium on details Retrieval study” from 2001. because the inception of the colloquium in 1979 the development has been hosted within the urban of Glasgow on 4 separate events. despite the fact that, this used to be the ?rst time that the association of the colloquium have been together undertaken by means of 3 separate machine and knowledge technology departments; a sign of the collaborative nature and variety of IR examine in the universities of the West of Scotland. The organizers of ECIR 2002 observed a pointy elevate within the variety of move- caliber submissions in resolution to the decision for papers over prior years and as such fifty two submitted papers have been every one allotted three contributors of this system committee for double blind overview of the manuscripts. a complete of 23 papers have been finally chosen for oral presentation on the colloquium in Glasgow which gave an popularity fee of under forty five% and ensured a truly excessive general of the papers presented.
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Extra info for Advances in Information Retrieval: 24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25–27, 2002 Proceedings
We think, however, that this is necessary, as the corpus is not totally indexed. Actually, relevant images may not be indexed, and visual retrieval — combined with the tunnel mechanism — is the only chance we have to retrieve those images. Figure 5 gives an example of our results: whereas textual descriptions allows the retrieval of two interesting images only (step 1), the tunnel mechanism (step 2) acts as a relay and allows retrieval of 6 more images, that are not actually indexed. The virtual image and the relevance feedback process are responsible for improving image ranking, and therefore to allow those non-indexed images to be presented.
Step 1 – Textual dendrogram search. — The thematic hierarchy is searched using the textual part of the virtual image (cosine measure is evaluated between “positive” part of the query and the cluster centroid). Depending on clusters size, though, not all images may be equally relevant to the query: this motivates an additional textual ranking process that will be used for the tunnel mechanism (see below). Let us suppose that cluster C1 is selected, in which image A is the best ranked. Step 2 – Visual dendrogram search (direct).
Croft. Document clustering: an evaluation of some experiments with the Cranﬁeld 1400 collection. Information processing and management, 11:171–182, 1974. 18. G. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983. 19. S. Satoh, Y. Nakamura, and T. Kanade. Name-It: Naming and detecting faces in news videos. IEEE MultiMedia, 6(1):22–35, January-March 1999. 20. S. Sclaroﬀ, M. La Cascia, S. Sethi, and L. Taycher. Unifying textual and visual cues for content-based image retrieval on the world wide web.