Superpixel clustering method and equipment based on multiple features

Abstract

The invention provides a superpixel clustering method and equipment based on multiple features, and the method fuses the boundary features and texture features of an image, and achieves the superpixelsegmentation bycombining with the multiple features of the image. The method comprises a clustering stage and a merging stage. The method comprises the steps of:in the clustering stage, judging the affiliation of the pixels by measuring the similarity between the pixels, and clustering to obtain pre-segmented super-pixels; and enabling the similarity measurement of the clustering stage tostart from color features and spatial position features of pixels, and adding boundary feature factors to adjust the similarity degree between pixel points located in a boundary adjacent region. The edges ofthe superpixels obtained in the process are tightly attached, but segmentation is too fine, and therefore the superpixels need to be further corrected. In the merging stage, the similarity degree between the superpixels is measured according to the uniqueness of the content of the superpixels, and the scattered superpixels are aggregated to obtain the final superpixels. Compared with an existing method, the method has the advantage that higher performance is shown in the aspect of keeping the super-pixel boundary and the object boundary in the image fit.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a multi-feature-based superpixel clustering method and device. Background technique [0002] Clustering algorithm, also known as group analysis, is a statistical analysis method for studying sample or index classification problems, and it is also an important algorithm for data mining. A clustering algorithm is composed of several patterns, usually, a pattern is a vector of measures, or it may be a point in space. [0003] In the technical field of image processing, clustering algorithms often only use color distance and spatial distance when measuring the similarity of pixels, so the generated superpixels generally have low edge fit, and the segmentation quality of image boundary regions is low. , cannot meet the needs of use. Contents of the invention [0004] In order to overcome the deficiencies in the above-mentioned prior art, the present invention provides a m...

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Problems solved by technology

[0003] In the technical field of image processing, clustering algorithms often only use color distance and spatial distance when measuring the similarity of p

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