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

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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Problems solved by technology

However, the dimensionality of water flow images is too high, which

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