A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image

Several CBIR schemes have been devised by the formation of the low dimensional image feature vector from the primitive visual features such as color, texture and/or shape of the image to speed up the retrieval process. In this paper, the visual contents of the images have been extracted using block level Discrete Cosine Transformation (DCT) and Gray Level Co-occurrence Matrix (GLCM). Since the DC coefficients based feature vector has retained the most significant visual components of the image, so initially, we have computed DC coefficients based uniform quantized histogram and some statistical parameters are derived from that histogram for the formation of the DC feature vector. Subsequently, other significant visual features are computed from the residual image where the residual image is obtained by taking the difference between the original image plane and the DC coefficients based reconstructed image plane. Thereafter, some statistical parameters from GLCMs of the residual image are considered for the construction of the GLCM based feature vector. This feature vector is suitable to identify the texture features of the residual image in a more effective way. The single feature vector has been obtained by combining DC and GLCM feature vectors since the combined extracted features from the images increase the accuracy of any image retrieval system. We have tested the scheme either in intensity image or on three color planes of an RGB color image. The experimental results are evaluated on two standard image databases and demonstrate the effectiveness of the proposed scheme in terms of average retrieval accuracy. In addition, the overall speed of the proposed CBIR system is high due to the formation of the low dimensional significant feature vector. The comparative results also show that the proposed scheme provides effective accuracy than some other state-of-the-art CBIR schemes.