A binary image feature extraction method and system

Abstract

The invention relates to the technical field of image retrieval and provides a binary image feature extraction method. The method comprises the steps of obtaining the position of a feature point according to preset rules; selecting a plurality of pixel points as binary comparison points; performing Gaussian blur treatment on the binary comparison points with each binary comparison point as the center; selecting a plurality of comparison pairs with the greatest distinction degree and the least correlation from all the binary comparison pairs formed from the binary comparison points; performing pixel attribute comparison on the selected plurality of binary comparison pairs to generate binary descriptors; converting the binary descriptors into integers according to preset rules. The invention also provides a binary image feature extraction system used for implementing the method. Thus, based on the principle of image forming of the retina of the human eye, the efficiency of a follow-up image matching retrieval algorithm is greatly increased.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to a binary image feature extraction method and system. Background technique [0002] The rapid growth of visual information such as Internet images has brought great challenges to the organization and management of information. There is an increasing demand for content analysis and detection of massive images. Image copy detection, image indexing, and content-based image search engines, etc. Technology came into being. Currently, these technologies generally use local feature point (Local feature point) features as features of image content. As the name suggests, local features are features that only appear in some parts of the picture. When the object is not completely occluded, these local features can exist stably and have good distinguishability, so these local features can be used as the whole image. Characteristics. [0003] The extraction of local feature point fe...

AI Technical Summary

Problems solved by technology

Representatives of real-number descriptors mainly include SIFT, SURF, etc. Each feature component of a real-number descriptor is a real number, so the description range is wide and the degree of discrimination is high, but its calculation is large, and the calculation in the subsequent feature matching is complicated. The algorithm is difficult and not suitable for real-time computing

Representatives of binary descriptors mainly include ORB, BRIEF, BRISK, FREAK, etc. Since each feature component of binary descriptors is a simple 01 binary, although the discrimination of a single description component is not strong, due to its simple calculation and occupied storage The space is small, and multiple sets of feature components can be calculated to improve the descriptive power. However, the number of feature components will affect the efficiency of the subsequent feature matching algorithm. Therefore, how to balance the feature discrimination and algorithm efficiency is the difficulty of the local feature point descriptor extraction algorithm. , in addition, as the number of images in the image library increases, the matching speed of both real-number descriptors and binary descriptors will decrease significantly. How to make the decrease as small as possible is still the difficulty of the local feature point descriptor extraction algorithm

[0006] In summary, there are obviously inconveniences and defects in the actual use of the existing technology, so it is necessary to improve

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