Feature description based on center-symmetric local mapped patterns

Local feature description has gained a lot of interest in many applications, such as texture recognition, image retrieval and face recognition. This paper presents a novel method for local feature description based on gray-level difference mapping, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor is invariant to image scale, rotation, illumination and partial viewpoint changes. Furthermore, this descriptor more effectively captures the nuances of the image pixels. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. In our experiments, the descriptor is compared to the Center-Symmetric Local Binary Pattern (CS-LBP). The results show that our descriptor performs favorably compared to the CS-LBP.