Quantized histogram color features analysis for image retrieval based on median and Laplacian filters in DCT domain

The efficient feature extraction and effective similar image retrieval are important steps for effective content-based image retrieval (CBIR) system. The extraction of features in compressed domain is attractive area due to the representation of almost all images in compressed format at present using DCT (Discrete Cosine Transformation) blocks transformation. During compression some critical information is lost and the perceptual information is left only, which has significant energy for retrieval in the compressed domain. In this paper, the statistical color features are extracted from the quantized histograms in the DCT domain using only the DC and the first three AC coefficients of the DCT blocks of image having more significant information. We study the effect of filters in image retrieval using the color features. We perform the experimental comparison of results in terms of precision of the median, median with edge extraction and the Laplacian filters using the color quantized histogram features in the DCT domain. The experimental results of the proposed approach using the Corel image database show that the Laplacian filter with the sharpened images give good performance in retrieval of the JPEG format images as compared to the median filter in the DCT frequency domain.