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Feature description based on Mean Local Mapped Pattern

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 Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. Furthermore, this descriptor more effectively captures the nuances of the image pixels. In our experiments, the descriptor is compared to the Center-Symmetric Local Mapped Pattern (CS-LMP) and the Center-Symmetric Local Binary Pattern (CS-LBP). The results show that our descriptor performs better compared to these two methods.