Content-based image retrieval using grid-based indexing and grey relational analysis

In this paper, an efficient two-stage approach is proposed for content-based image retrieval (CBIR). In establishing the database, the features of an image are extracted from its color histograms and discrete cosine transform (DCT) coefficients. To improve the retrieval performance, the quantization technique is applied to quantize the vector of color histograms such that the feature space is partitioned into a finite number of grids, each of which corresponds to a grid code (GC). At the first stage, a reduced set of candidate images which have the same GC (or adjacent GCs) as that of the query image is obtained. At the second stage, the remaining candidates are examined by using grey relational analysis on the significant DCT coefficients. The experimental results show that the proposed approach leads to a fast retrieval with good accuracy.