Á trous gradient structure descriptor for content based image retrieval

This paper first introduces á trous wavelet correlogram feature descriptor for image representation. By further extension in this descriptor, á trous gradient structure descriptor (AGSD) is proposed for content-based image retrieval. AGSD facilitates the feature calculation with the help of á trous wavelet’s orientation information in local manner. The local information of the image is extracted through microstructure descriptor (MSD); it finds the relations between neighborhood pixels. Finally, relation among á trous quantized image and MSD image is used for final feature extraction. The experiments are performed on Corel 1000, Corel 2450, and MIRFLICKR 25000 databases. Average precision, weighted precision, standard deviation of weighted precision, average recall, standard deviation of recall, and rank, etc., of proposed methods are compared with optimal quantized wavelet correlogram, Gabor wavelet correlogram, and combination of standard wavelet filter and rotated wavelet filter correlogram. It is concluded that the proposed methods have improved the retrieval performance significantly.