Method for describing local characteristic of image

Abstract

The invention discloses a method for describing a local characteristic of an image, which is used for describing characteristic manifold of a characteristic point extracted through a size-invariable feature extraction method. The method is characterized in that: a series of affine transformation is processed for a local area of the characteristic point, a size-invariable characteristic description vector of the characteristic point is correspondently extracted from each variable image to form a characteristic vector set, and the characteristic vector set is further simulated through a linear subspace set to be used as a characteristic descriptor of the characteristic manifold. The characteristic description method comprises the following steps that: the local area image of the characteristic point is extracted, a series of transformation is processed for the local image, the size-invariable characteristic description is extracted for the characteristic point of each variable image, the characteristic vector set is formed, the linear subspace is adopted to approach the characteristic vector set, and the characteristic descriptor is generated. The input of the method is a series of characteristic points which are expressed by coordinate positions, and the output of the method is the characteristic description expressed by a plurality of linear subspaces.

Description

technical field [0001] The invention relates to a computer image processing method, in particular to an image local feature description method with strong descriptive and distinguishing power. Background technique [0002] Image local feature description is a basic and hot issue in the fields of computer vision and image processing. An image local feature description method with strong descriptiveness, good invariance and discrimination can be used in image registration and splicing, object tracking, object tracking, etc. Recognition and image retrieval have applications. [0003] Compared with the overall feature, the local feature marks the important areas in the image, and the image information is represented by these areas, which can save the calculation amount of the application while representing the important local information of the image. Traditional local feature descriptions, such as scale invariant feature SIFT (Scale Invariant Feature Transformation), generally...

AI Technical Summary

Problems solved by technology

[0004] However, research in recent years has shown that the invariance range of traditional local feature descriptions for affine transformations of image shooting viewpoints is quite limited, which severely limits the application of local feature descriptors in areas such as image registration and stitching.

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