Color and local phase based descriptor for human detection

In this paper we present a new descriptor based on phase congruency concept and LUV color space features. Since the phase of the signal conveys more information regarding signal structure than the magnitude and the indispensable quality of the color in describing the world around us, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the three color image channels. The maximum phase congruency values are selected from the corresponding color channels. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the proposed descriptor; called Color Histogram of Oriented Phase (CHOP). Several experiments were performed to evaluate the performance of the CHOP descriptor. The experimental results show that the proposed descriptor has better detection performance and lower error rates than a set of the state of the art feature extraction methodologies.