Image retrieval based on energy histograms of the low frequency DCT coefficients

With the increasing popularity of the use of compressed images, an intuitive approach for lowering computational complexity towards a practically efficient image retrieval system is to propose a scheme that is able to perform retrieval computation directly in the compressed domain. In this paper, we investigate the use of energy histograms of the low frequency DCT coefficients as features for the retrieval of DCT compressed images. We propose a feature set that is able to identify similarities on changes of image-representation due to several lossless DCT transformations. We then use the features to construct an image retrieval system based on the real-time image retrieval model. We observe that the proposed features are sufficient for performing high level retrieval on medium size image databases. And by introducing transpositional symmetry, the features can be brought to accommodate several lossless DCT transformations such as horizontal and vertical mirroring, rotating, transposing, and transversing.