Spatio-temporal saliency detection using phase spectrum of Quaternion Fourier Transform

Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), Neuromorphic Vision Toolkit (NVT) and etc., but they, demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier tran form called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. The results are good, but the reason is questionable. In this paper, we propose it is the phase spectrum, not the amplitude spectrum, of the Fourier transform that is the key in obtaining the location of salient areas. We provide some examples to show that PFT can get better results in comparison with SR and requires less computational complexity as well. Furthermore, PFT can be easily extended from a two-dimensional Fourier transform to a Quaternion Fourier Transform (QFT) if the value of each pixel is represented as a quaternion composed of intensity, color and motion feature. The added motion dimension allows the phase spectrum to represent spatio-temporal saliency in order to engage in attention selection for videos as well as images. Extensive tests of videos, natural images and psychological patterns show that the proposed method is more effective than other models. Moreover, it is very robust against white-colored noise and meets the real-time requirements, which has great potentials in engineering applications.