Face image clustering method based on golden section method

A golden section method, face image technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as poor performance, and achieve the effect of improving performance, ensuring accuracy and effectiveness

Active Publication Date: 2019-12-03
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the poor performance of existing face image clustering methods and to significantly improve the fac

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  • Face image clustering method based on golden section method
  • Face image clustering method based on golden section method
  • Face image clustering method based on golden section method

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[0050] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0051] Reference Figure 1 ~ Figure 3 , A face image clustering method based on the golden section method, including 3 parts: the application of DCNN to achieve the representation of face images (such as figure 1 ), K-Means++ clustering algorithm realizes the recognition and classification of a large number of face representations (such as figure 2 ), and the optimal number of clusters for one-dimensional search using the golden section algorithm (such as image 3 ) To improve clustering performance. Including the following steps:

[0052] 1) Apply the deep convolutional neural network DCNN to realize the feature representation of all face images in the database. The process includes preprocessing, face alignment and feature extraction. The steps are as follows:

[0053] Step 1.1: Preprocessing is to make preliminary corrections to the image. For example, social ...

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Abstract

The invention discloses a face image clustering method based on a golden section method. The face image clustering method comprises the following steps: 1) applying a deep convolutional neural networkDCNN to realize feature representation of all face images in a database; 2) applying a K-Means ++ clustering algorithm to realize clustering of image representation; and 3) determining the optimal clustering number based on a 0.618 golden section method, and the process is as follows: firstly, giving a clustering range [a, b], and K belongs to [a, b]; randomly initializing a given clustering number K0 in the range, and constructing an optimization function f(K) based on an internal performance evaluation index of a clustering result; then, based on a 0.618 golden section optimization algorithm, dynamically searching the optimal solution of the function for in a one-dimensional mode; wherein the optimal solution is the optimal clustering number K*, and the corresponding clustering result C* is the optimal clustering of the face image library. According to the invention, the face image clustering performance is significantly improved.

Description

technical field [0001] The invention relates to a face image clustering method, in particular to a face image clustering method based on the golden section method. Background technique [0002] With the rapid development of computer vision and pattern recognition technology, image, as the most common visual information presentation mode, has broad application prospects. In the era of "big data", a large number of pictures are generated every day. For example, on social media, according to Facebook reports, an average of 350 million pictures are generated every day, most of which are face images. In judicial investigations, there are still a huge number of pictures that urgently need to be identified and classified. In terms of social security maintenance and monitoring management, a large number of face images captured by cameras need to be authenticated and stored for comparison. However, these face images usually do not have identity tags, or the tags are missing. In t...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/161G06V40/168G06N3/045G06F18/23213
Inventor 钱丽萍俞宁宁周欣悦吴远黄亮
Owner ZHEJIANG UNIV OF TECH
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