Abstract: In order to solve the problem that it is difficult to balance the real-time performance and robustness in image matching using local feature, an image matching algorithm based on Signed Contrast Context Histogram (SCCH) feature descriptor is presented. Multi-scale feature points are extracted with Harris operator in Gaussian pyramid images to reduce the data for processing. Feature descriptor is built with the means of the differences of gray value in the sub-regions of feature point neighborhood, which not only decreases the complexity of building descriptor and the dimensions of descriptor, but also enhances the robustness and distinctiveness of descriptor. Furthermore, the absolute distance between descriptors is used to match feature points as a similarity measurement to lessen the computation. Simulation results indicate that the proposed algorithm keeps invariant in the case of image zoom, rotation, blurring, luminance varying as well as smaller view angle changes, and its matching speed is faster.