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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.

The aim of this paper is to introduce a new methodology for micro-pattern analysis in digital images. The gray-level pixels' structure in an image neighborhood describes a spatial specific context. Edge, line, spot, blob, corner or texture can be described by this structure. The gray-level values of the image pixel are interpreted as a fuzzy set, and each pixel gray-level as a fuzzy number. A membership function can be defined to describe the membership degree of the central pixel to the others in an image neighborhood. We have called this method the Local Fuzzy Pattern (LFP). If a sigmoid membership function is used, the proposed methodology describes the texture very well, and if a symmetrical triangular membership function is applied, the LFP is better for edge's detection. The results were compared to the Local Binary Pattern (LBP), for texture classification getting the better hit-rate. Our proposed formulation for the LFP is a generalization of previously published techniques, such as Texture Unit, LBP, FUNED, and Census Transform.

Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new 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. 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. The proposed descriptor more effectively captures smaller differences of the image pixels than similar ones. In our experiments, we implemented an object recognition system based on the M-LMP and compared our results to the Center-Symmetric Local Binary Pattern (CS-LBP) and the Scale-Invariant Feature Transform (SIFT). The results for object classification were analyzed in a BoF methodology and show that our descriptor performs better compared to these two previously published methods.

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