In this paper, a hierarchical grid-based indexing method for content-based image retrieval (CBIR) is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing database, quantization technique is applied to quantize the DCT coefficients of each database image, such that the feature space is partitioned into a finite number of grids, each of which corresponds to a grid code (GC). On querying an image, a reduced set of candidate images which have the same GC as that of the query image is obtained at varying levels of grid granularity. In the fine matching stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that the proposed method leads to a fast retrieval with good accuracy.
Content-based image retrieval (CBIR) techniques would allow indexing and retrieving images based on their low-level contents, which involves a large number of image pixels and thus becomes an inherently and essentially computational intensive task. This paper proposes a distance threshold pruning (DTP) method to alleviate computational burden of CBIR without sacrificing its accuracy. In our approach, the images are converted into the YUV color space, and then transformed into discrete cosine transform (DCT) coefficients. Benefited from the energy compacting property of DCT, Only the low-frequency DCT coefficients of Y, U, and V components are stored. On querying an image, at the first stage, the DTP serves as a filter to remove those candidates with widely distinct features. At the second stage, the detailed similarity comparison (DSC) is performed on those remaining candidates passing through the first stage. The experimental results show that both high efficacy and high data reduction rate can be achieved simultaneously by using the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.