Title: MCM-CBIR: Multi Clustering Method for Content Based Image Retrieval
Authors: Lacheheb, Hadjer
Aouat, Saliha
Hamouchene, Izem
Citation: WSCG 2013: Communication Papers Proceedings: 21st International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS Association, p. 159-165.
Issue Date: 2013
Publisher: Václav Skala - UNION Agency
Document type: conferenceObject
konferenční příspěvek
URI: http://wscg.zcu.cz/WSCG2013/!_2013-WSCG-Communications-proceedings.pdf
ISBN: 978-80-86943-75-6
Keywords: na obsahu založené vyhledávání obrazů;vyhledávací systémy
Keywords in different language: content based image retrieval;retrieval systems
Abstract: Image retrieval systems are designed to provide the ability of searching and retrieving images in huge image databases. A content based image retrieval system (CBIR) is used to offer such tasks based on the content of the image. In this paper we propose a new method of CBIR system based on a learning technique. Our method uses k-means clustering to reduce data and to improve the system performance. The specificity of our approach is the use of each feature vector separately in the clustering process in order to obtain different clustering on the same database, differently to other approaches that combine features vectors to cluster the database. For this reason we call it multi-clustering approach. The advantage of this approach consists in keeping the performance of the features and getting several views of the database due to the separation of features. The experimental results show the efficiency of our approach.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2013: Communication Papers Proceedings

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