Method for constructing compact image local feature descriptor

Abstract

The invention relates to a method for constructing a compact image local feature descriptor. Compared with the prior art, the defects that an image local descriptor with high descriptive power is high in dimensionality, so that feature matching computing cost is large, and a common dimensionality reduction method influences the distinction degree and visuality of the image local feature descriptor are overcome. The method comprises the following steps that a feature region is determined; the feature region is partitioned and numbered; codes of leading central symmetry local binary patterns of points are worked out in the feature region; in units of partitioned sub-regions, a feature vector of the leading central symmetry local binary pattern of each partitioned sub-region is worked out; according to the sequence of the numbers of the partitioned sub-regions, the feature vectors of the leading central symmetry local binary patterns of all the partitioned sub-regions are arranged. The descriptor constructed with the method has the advantages of being low in dimensionality and high in descriptive power and distinction degree, high robustness of rotation transformation and illumination transformation of images is achieved, calculation is easy, and the matching speed is high.

Description

technical field [0001] The invention relates to the technical field of visual processing, in particular to a method for constructing a compact image local feature descriptor. Background technique [0002] In recent years, the local features of images have attracted the attention of many researchers. Because of their robustness to image deformation and light transformation and robustness to partial occlusion of images, they are widely used in vision applications, such as object retrieval, object recognition, etc. , face recognition, behavior classification, etc. The ideal local feature descriptor requires good description ability and discrimination, and can be quickly generated and matched. The low-dimensional characteristics of local descriptors are an important factor to ensure their fast matching, but many influential local feature descriptors have high dimensions. For the same local feature descriptor, reducing its dimension may reduce its corresponding degree of discrim...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the defects that the high dimensionality of image local descriptors with strong descriptive ability leads to high cost of feature matching calculation, and the dimensionality reduction method affects the discrimination and intuitiveness of image local feature descriptors, and provides a compact The construction method of image local feature descriptor to solve the above problems

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