Image matching method based on random sampling hash representation

A technology of random sampling and matching method, applied in the field of information retrieval, can solve the problem of poor coding effect, etc., and achieve the effect of high accuracy and good effect.

Active Publication Date: 2014-12-17
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

In practice, this approach has been shown to lead to a problem where the encoding gets worse as the number of bits increases

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  • Image matching method based on random sampling hash representation

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Embodiment Construction

[0032] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0033] figure 1 is the flow chart of the image matching method based on random sampling binary representation of the present invention, such as figure 1 As shown, the method includes the following steps:

[0034] Step 1, form n images into an original data set, and extract the visual features of all images to represent the corresponding images, the visual features form a feature space, the scale of the original data set is n, and the features of the feature space The total dimension is d;

[0035] Wherein, the visual feature may be: a color histogram, a SIFT feature, and a GIST feature.

[0036] Step 2, randomly select m images from the original data set as training samples to form a sample space, wherein m is much sma...

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Abstract

The invention discloses an image matching method based on random sampling hash representation. The method includes the following steps: forming an original data set through n images, extracting visual characteristics of all the images to generate a characteristic space, randomly selecting m images from the original data set, randomly extracting p visual characteristic subsets in the characteristic space to obtain a sample subset, learning t main characteristic vectors of the obtained sample subset to serve as a Hash projection function, generating a t-bit two-value hash code, repeating the steps for k times to obtain k sections to t-bit two-value hash code, conducting cascading to obtain the k*t-bit two-value hash code to serve as the matching characteristic, acquiring two-value hash codes of the images to be matched and each image in the original data set, conducting similarity measurement based on the obtained two-value hash codes to obtain of a matching result of the images to be matched. By means of the method, the accuracy of a similar adjacent searching method based on the hash codes can be improved, and the method is applicable to image retrieval, image matching and other machine learning algorithms.

Description

technical field [0001] The invention relates to the field of information retrieval, in particular to an image matching method based on random sampling hash representation. Background technique [0002] In recent years, with the massive growth of data, we have entered the era of big data. Big data poses great challenges to traditional machine learning and pattern recognition methods. Image matching is one of the key technologies in many pattern recognition problems and applications, that is, to find the most similar image by comparing the query image with the image in the database. Traditional linear image matching methods, such as: exhaustive search (exhaustive search) method, are very slow in the case of large-scale data, and are impractical in many applications that require relatively high speed. For example, in image retrieval applications, users have certain requirements for query time, and the time required for exhaustive search is often unacceptable. [0003] In ord...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/64G06F17/30
Inventor 程健冷聪卢汉清
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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