Clustering and dimensionality reduction for image retrieval in high-dimensional spaces

• Autorzy: Benkrama S. , Zaoui L. , Charrier C.
• Języki publikacji: EN

Abstrakty

EN

The scalability of indexing techniques and image retrieval pose many problems. Indeed, their performance degrades rapidly when the database size increases. In this paper, we propose an efficient indexing method for high-dimensional spaces. We investigate how high-dimensional indexing methods can be used on a partitioned space into clusters to help the design of an efficient and robust CBIR scheme. We develop a new method for efficient clustering is used for structuring objects in the feature space; this method allows dividing the base into data groups according to their similarity, in function of the parameter threshold and vocabulary size. A comparative study is presented between the proposed method and a set of classification methods. The experiments results on the Pascal Visual Object Classes challenges (VOC) of 2007 and Caltech-256 dataset show that our method significantly improves the performance. Experimental retrieval results based on the precision/recall measures show interesting results.

Słowa kluczowe

EN

features extraction; high-dimensional indexing; bag of visual words; clustering

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 8, nr 3
• Strony: 37--50
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Benkrama S.

• Faculty of Mathematics and Computer Science, Department of Computer Science, University of Sciences and Technology of Oran Mohamed Boudiaf, Algeria

autor

Zaoui L.

• Faculty of Mathematics and Computer Science, Department of Computer Science, University of Sciences and Technology of Oran Mohamed Boudiaf, Algeria

autor

Charrier C.

• GREYC, UMR CNRS 6072, University of Caen Basse-Normandie, Caen, France

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