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Feature detection method and apparatus

A feature detection and feature value technology, applied in the field of image processing, can solve problems such as low reliability and feature misalignment

Active Publication Date: 2016-02-24
ZHEJIANG UNIVIEW TECH
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Problems solved by technology

[0003] Since the above method is directly based on the original video image to generate the corresponding high-dimensional feature vector, its reliability is low in the case of rotation, translation and large noise
On the other hand, due to the direct use of cascading methods to generate high-dimensional feature vectors, feature misalignment will occur during feature detection

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  • Feature detection method and apparatus

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Embodiment Construction

[0037] Aiming at the problems existing in the prior art, an embodiment of the present invention proposes a method for feature detection, which can be applied to a system that performs feature detection in a FisherVector (Fisher vector) manner. Among them, the FisherVector method is a feature extraction method based on a mixed Gaussian model. By using K Gaussian kernels to simulate the distribution of local features for video images, local features can be effectively fused, and it has strong reliability for changes in video images. Therefore, the FisherVector method is an effective feature encoding method. However, when the FisherVector method generates feature vectors, it needs to use more Gaussian kernels to generate a GMM (GaussianMixtureModel, Gaussian mixture model) model. For example, it usually needs to use K Gaussian kernels, and the value of K is generally 256-512. The K value is large, so the calculation complexity of generating the GMM model is high, the calculation ...

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Abstract

The present invention provides a feature detection method and apparatus and aims at each to-be-detected video image. The method comprises: carrying out feature extraction on video images, and obtaining M eigenvalues; carrying out sampling on the M eigenvalues for N times, and obtaining N sampling results, wherein each sampling result comprises a part of eigenvalues in the M eigenvalues; aiming at each sampling result, adopting k Gaussian kernels for the sampling results, generating a GMM sub-model corresponding to the each sampling result, and obtaining N GMM sub-models; and after ranking the N GMM sub-models, obtaining corresponding GMM models, obtaining eigenvectors corresponding to the GMM models, and carrying out feature detection by using the eigenvectors. According to the technical scheme provided by the present invention, the corresponding GMM models can be generated by using a small number of K Gaussian kernels, thereby reducing calculation complexity of a feature detection algorithm, improving calculation performance, improving algorithm convergence, and effectively accelerating eigenvector generation.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and device for feature detection. Background technique [0002] Feature detection has a wide range of applications, such as video analysis, object detection, image recognition, etc. Typically, the process of feature detection includes: dividing a video image into several sub-regions, performing feature extraction on each sub-region, obtaining the feature value of each sub-region, and finally cascading the extracted feature values ​​to generate a set of high-dimensional A feature vector is used, and the video image is represented by the high-dimensional feature vector, so that the feature detection of the video image is performed based on the high-dimensional feature vector of the video image. [0003] Since the above method generates a corresponding high-dimensional feature vector directly based on the original video image, its reliability is low in the...

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Application Information

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IPC IPC(8): G06K9/46
CPCG06V10/50
Inventor 毛敏
Owner ZHEJIANG UNIVIEW TECH
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