Image local feature extracting method on basis of Hilbert curve and LBP (length between perpendiculars)

A technology of local features and extraction methods, applied in the field of pattern recognition, can solve the problem of not considering the relationship of local binary features, and achieve the effect of improving performance

Active Publication Date: 2012-09-12
北京博研高科技术有限公司
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Problems solved by technology

But this method does not consider the relationship between local binary features in a certain area
In fact, the spatial relationship between feature points in the region is used to extract more detailed information, so the local binary mode has certain limitations when describing images.

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  • Image local feature extracting method on basis of Hilbert curve and LBP (length between perpendiculars)
  • Image local feature extracting method on basis of Hilbert curve and LBP (length between perpendiculars)
  • Image local feature extracting method on basis of Hilbert curve and LBP (length between perpendiculars)

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Embodiment Construction

[0018] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0019] The object extraction method based on the hilbert curve and lbp proposed by the present invention extracts spatially ordered features for input images or objects. The specific implementation steps are as follows:

[0020] Step 1: Get the input object information.

[0021] The input object information refers to images input through cameras or various sensors, such as images such as human faces and palm prints, which are subjected to Gabor transform processing.

[0022] Step 2, LBP feature extraction.

[0023] For each pixel on the image, take the 8 adjacent pixels around it, and make a difference with the gray value of these 8 points, and then encode the difference result according to the threshold value to form an eight-bit binary number. The decimal number corresponding to the binary is the LBP feature value of the pixel.

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Abstract

The invention relates to an image local feature extracting method on the basis of a Hilbert curve and the LBP (length between perpendiculars). In the method, the Hilbert curve is adopted to arrange LBP features of an image according to a certain sequence and the sequence can ensure an obtained feature vector to comprise a space neighboring relation among feature points so as to improve the identification performance of a mode identification system. The local feature extracting method particularly comprises the following steps of: a first step of acquiring an input object and carrying out pretreatment of filtering, removing noise and the like; a second step of extracting the LBP features; and a third step of selecting the Hilbert curve and extracting a high-order feature. The method disclosed by the invention is easy to implement, only relates to simple difference, binaryzation and feature ordering and has low complexity, i.e. low calculation complexity of both difference and binaryzation.

Description

technical field [0001] The invention relates to a pattern recognition feature extraction method, which belongs to the technical field of pattern recognition. Background technique [0002] Feature extraction in the field of image recognition is a crucial step, and texture feature extraction methods are currently a hot spot. References: T. Ahonen, A. Hadid, and M. "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.12, pp.2037-2041, 2006. A local binary pattern feature is introduced, which is An important method in the current pattern recognition field, it can extract the distribution of texture features in images, and has achieved very good results in many texture recognition and analysis fields. But this method does not consider the relationship between local binary features in a certain region. In fact, the spatial relationship between feature points in the region is u...

Claims

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Application Information

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IPC IPC(8): G06K9/46
Inventor 薄占滨
Owner 北京博研高科技术有限公司
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