A Fast and Effective Model for Cyclic Analysis and Its Application in Classification

摘要

In this paper, a new feature extraction technique for content-based image retrieval is proposed. This method is based on cyclic function (CF) that provides a second-order statistical description in the frequency domain of signals. Then, the CF of each signal is calculated by FFT accumulation method which is a computational efficient algorithm. Features are energy and standard deviation of CF of signals got from image at different regions of bifrequency plane. This scheme shows high performance in both image sets. The image is partitioned into non-overlapping tiles of different sizes. The features drawn from transferred image with proposed new features, CF using first and second moments between the image tiles, serve as local descriptors of texture. This local information is captured for two resolutions and two grid layouts that provide different details of the same image. Shape information is captured in terms of edge. Invariant moments are then used to record the shape features. The combination of the texture features between image and the shape features provide a robust feature set for retrieval. The experimental results show the efficacy of the method. The experimental results are compared with two image sets at previous works and are found to be encouraging.