Scale-invariant feature transform (SIFT) algorithm has been successfully applied to object recognition and to image feature extraction, which is a major application in the field of image processing. Nonetheless, the SIFT algorithm has not been solved effectively in practical applications that requires real-time performance, much calculation, and high storage capacity given the framework level and the iterative calculation process in the SIFT Gaussian blur operation. The extraction of image feature information is accelerated using the speeded-up robust features algorithm. However, this algorithm remains sensitive to complicated deformation. To address these problems, in this paper, we proposes a novel algorithmic framework based on bidimensional empirical mode decomposition (BEMD) and SIFT to extract self-adaptive features from images. First, the BEMD algorithm is used to decompose the self-adaptive features of the original image and to obtain multiple BIMF components. Second, the SIFT algorithm optimizes the extraction of parameters that reflect characteristic information on BIMF components. Related parameters are obtained through genetic algorithm optimization. Third, the method for extracting the characteristic information of the BIMF components involves synthesizing all of the accumulated characteristic information in the original image. Comparison results show that the method of calculating image feature extraction speed, accuracy, and reliability has a stronger effect than other methods.
['Feng-Ping An', 'Xianwei Zhou']
BEMD–SIFT feature extraction algorithm for image processing application