Query refinement and user relevance feedback for contextualized image retrieval
|Query refinement and user relevance feedback for contextualized image retrieval|
|Author(s)||Chandramouli K., Kliegr T., Nemrava J., Svatek V., Izquierdo E.|
|Published in||IET Conference Publications|
|Keyword(s)||K-Means, Particle swarm optimisation, Query refinement, Relevance feedback, Wikipedia (Extra: K-Means, Particle swarm optimisation, Query refinement, Relevance feedback, Wikipedia, Clustering algorithms, Feedback, Image retrieval, Information services, Motion Picture Experts Group standards, Visual communication, Particle swarm optimization (PSO))|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Query refinement and user relevance feedback for contextualized image retrieval is a 2008 conference paper written in English by Chandramouli K., Kliegr T., Nemrava J., Svatek V., Izquierdo E. and published in IET Conference Publications.
The motivation of this paper is to enhance the user perceived precision of results of content based information retrieval (CBIR) systems with query refinement (QR), visual analysis (VA) and relevance feedback (RF) algorithms. The proposed algorithms were implemented as modules into K-Space CBIR system. The QR module discovers hypernyms for the given query from a free text corpus (such as Wikipedia) and uses these hypernyms as refinements for the original query. Extracting hypernyms from Wikipedia makes it possible to apply query refinement to more queries than in related approaches that use static predefined thesaurus such as Wordnet. The VA Module uses the K-Means algorithm for clustering the images based on low-level MPEG - 7 Visual features. The RF Module uses the preference information expressed by the user to build user profiles by applying SOM- based supervised classification, which is further optimized by a hybrid Particle Swarm Optimization (PSO) algorithm. The experiments evaluating the performance of QR and VA modules show promising results.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 2 time(s)