Robust and real-time object recognition based on multiple fractal dimension

We proposes a multiple fractal dimensions (MFD) method for robust object description. MFD is an effective feature extraction approach, which is first calculated based on a phase angle quantization method to categorize the points of the input image. And then fractal dimensions are calculated to describe the distribution of feature pattern characterized as the intrinsic property of the general objects, i.e., land scene, face and pedestrian. We theoretically proven that our MFD is shown to be invariant to local variations, i.e., Bi-Lipschitz, which is a desirable characteristic for objects, such as land-scene images, face and pedestrian due to the existence of scale variations, local variations and illumination variations in those images. The proposed method is extensively evaluated on land-use scene recognition, face recognition, expression recognition, and pedestrian detection. The experimental results on UC Merced 21-class scene dataset, AR, JAFFE and INRIA pedestrian databases show that our method achieves superior performances over several state-of-the-art methods in terms of recognition rates.