Difference between revisions of "Conceptual Image Retrieval over the Wikipedia Corpus"
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| + | {{Infobox work | ||
| + | | title = Conceptual Image Retrieval over the Wikipedia Corpus | ||
| + | | date = 2008 | ||
| + | | authors = [[Adrian Popescu]]<br />[[Hervé Le Borgne]]<br />[[Pierre-Alain Moëllic]] | ||
| + | | link = http://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-PopescuEt2008.pdf | ||
| + | }} | ||
'''Conceptual Image Retrieval over the Wikipedia Corpus''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Adrian Popescu]], [[Hervé Le Borgne]] and [[Pierre-Alain Moëllic]]. | '''Conceptual Image Retrieval over the Wikipedia Corpus''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Adrian Popescu]], [[Hervé Le Borgne]] and [[Pierre-Alain Moëllic]]. | ||
== Overview == | == Overview == | ||
Image retrieval in large-scale databases is currently based on a textual chains matching procedure, a technique that produces good results as long as the annotations associated to pictures are accurate and detailed enough. These conditions are not met for a large majority of image corpuses, such as the [[Wikipedia]] collection, and it is interesting to explore methods that go beyond chain matching. In this paper, authors present approach to image retrieval, tested in the ImageCLEF 2008 WikipediaMM. The approach is based on a query reformulation using concepts that are semantically related to those in the initial query. For each interesting entity in the query, authors used Wikipedia and [[WordNet]] to extract and list of related concepts, which were further ranked in order to propose the most salient in priority. Authors also made a list of visual concepts which were used in order to re-rank the answers to queries that included, implicitly or explicitly, these visual concepts. The CEA submitted two automatic runs, one based on query reformulation only and one combining query reformulation and visual concepts, which were ranked 4 th and 2 nd using the MAP measure. | Image retrieval in large-scale databases is currently based on a textual chains matching procedure, a technique that produces good results as long as the annotations associated to pictures are accurate and detailed enough. These conditions are not met for a large majority of image corpuses, such as the [[Wikipedia]] collection, and it is interesting to explore methods that go beyond chain matching. In this paper, authors present approach to image retrieval, tested in the ImageCLEF 2008 WikipediaMM. The approach is based on a query reformulation using concepts that are semantically related to those in the initial query. For each interesting entity in the query, authors used Wikipedia and [[WordNet]] to extract and list of related concepts, which were further ranked in order to propose the most salient in priority. Authors also made a list of visual concepts which were used in order to re-rank the answers to queries that included, implicitly or explicitly, these visual concepts. The CEA submitted two automatic runs, one based on query reformulation only and one combining query reformulation and visual concepts, which were ranked 4 th and 2 nd using the MAP measure. | ||
Revision as of 08:50, 9 May 2020
Authors | Adrian Popescu Hervé Le Borgne Pierre-Alain Moëllic |
|---|---|
Publication date | 2008 |
Links | Original |
Conceptual Image Retrieval over the Wikipedia Corpus - scientific work related to Wikipedia quality published in 2008, written by Adrian Popescu, Hervé Le Borgne and Pierre-Alain Moëllic.
Overview
Image retrieval in large-scale databases is currently based on a textual chains matching procedure, a technique that produces good results as long as the annotations associated to pictures are accurate and detailed enough. These conditions are not met for a large majority of image corpuses, such as the Wikipedia collection, and it is interesting to explore methods that go beyond chain matching. In this paper, authors present approach to image retrieval, tested in the ImageCLEF 2008 WikipediaMM. The approach is based on a query reformulation using concepts that are semantically related to those in the initial query. For each interesting entity in the query, authors used Wikipedia and WordNet to extract and list of related concepts, which were further ranked in order to propose the most salient in priority. Authors also made a list of visual concepts which were used in order to re-rank the answers to queries that included, implicitly or explicitly, these visual concepts. The CEA submitted two automatic runs, one based on query reformulation only and one combining query reformulation and visual concepts, which were ranked 4 th and 2 nd using the MAP measure.
