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On Projection Based Operators in lp Space for Exact Similarity Search

Wichert A., Moreira C.

Fundamenta Informaticae

2015

Vol. 136, nr 4

461--474

We investigate exact indexing for high dimensional lp norms based on the 1-Lipschitz property and projection operators. The orthogonal projection that satisfies the 1-Lipschitz property for the lp norm is described. The adaptive projection defined by the first principal component is introduced.

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

Benkrama S., Zaoui L., Charrier C.

Journal of Theoretical and Applied Computer Science

2014

Vol. 8, nr 3

37--50

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.