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Similarity Search in a Large Scale
Abstract
In this paper, we address the subject of large image database indexing for content-based retrieval. We investigate how high-dimensional indexing methods can be used in 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 database 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.
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