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Joint Laplacian regularization and adaptive feature learning-based water flow image clustering method

A self-learning and water flow technology, applied in the field of pattern recognition, can solve the problems of reducing the effectiveness of clustering methods and high dimensionality of water flow images, and achieve the effect of facilitating intelligent identification and classification management, and improving efficiency and accuracy

Active Publication Date: 2017-01-04
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, the dimensionality of water flow images is too high, which greatly reduces the effectiveness of traditional clustering methods

Method used

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  • Joint Laplacian regularization and adaptive feature learning-based water flow image clustering method
  • Joint Laplacian regularization and adaptive feature learning-based water flow image clustering method
  • Joint Laplacian regularization and adaptive feature learning-based water flow image clustering method

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[0068] Since the water flow image is taken outdoors and is affected by factors such as weather and light changes, the original image of the water flow is first converted into a grayscale image and histogram equalized, and the contrast is enhanced to make the water pattern outline that can reflect the flow velocity more accurate. obvious, Figure 2(a) with 2(b) They are the original image of water flow and the image equalized by histogram. The adaptive feature weight learning of Lass regularization on the image can effectively eliminate invalid features (such as reflective areas). In the experiment, there are 100 water flow images, the flow velocity covers 5 intervals, and each flow velocity interval contains 20 test pictures. The pixel of each water flow image is 1000×750, that is, d=750000. According to step 1, the gray value of the water flow image is expanded by column and concatenated into a column vector, and these 100 column vectors are used as elements to form the wat...

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Abstract

The invention relates to a joint Laplacian regularization and adaptive feature learning-based water flow image clustering method. The method includes the following steps of: step 1, preprocessing: the pixel values of water flow images are expanded in columns and concatenated into column vectors, and the column vectors, adopted as elements, constitute the feature matrix X=[x1,x2, ... ,xn] of a water flow image data set, wherein xi belongs to R<d*1>; step 2: joint Laplacian regularization and adaptive feature learning-based data clustering; and step 3, water flow image clustering: the water flow image data set is divided into c clusters according to a similarity matrix S having c block diagonal structures according to flow velocity features.

Description

technical field [0001] The invention relates to an image clustering method, in particular to a water flow image clustering method combined with Lars regular term and feature self-learning, and belongs to the field of pattern recognition. Background technique [0002] Water flow image clustering technology is widely used in a variety of direct or indirect measurements based on water flow, such as: water flow velocity measurement, flow calculation and water level detection, etc. necessary. Accurate and timely flow rate monitoring can significantly improve the scientificity of water conservancy project scheduling and the predictability of drought and flood disasters. When analyzing the water flow image, the water flow image is usually classified according to a certain water flow characteristic, for example, divided into intervals according to the flow velocity. The classification method requires a large amount of label information. However, as the number of water flow images ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 郑建炜李卓蓉鞠振宇杨平邱虹陈婉君
Owner ZHEJIANG UNIV OF TECH
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