Automatic K adjacent local search heredity clustering method for graphic image

Abstract

The invention discloses an automatic k adjacent local search heredity clustering method for a graphic image, which mainly overcomes the defect of the conventional automatic clustering algorithm that the local optimization is easy to cause. The automatic k adjacent local search heredity clustering method comprises the realization steps of: (1) detecting an outline of an image by utilizing a canny edge detector; (2) describing the outline of the image by utilizing a shape context method and calculating a matched cost matrix of an outline point; (3) matching the outline point by utilizing a dynamic programming method according to the matched cost matrix; (4) converting the matched outline point by utilizing a procrustes analysis method; (5) representing the converted matched outline point and measuring an edit distance between character strings; (6) calculating the distance between the images according to the edit distance of the character strings; (7) clustering the images by utilizing a heredity automatic clustering method; and (8) carrying out k adjacent local search on groups of a heredity method. The automatic k adjacent local search heredity clustering method for the graphic image has the advantages that overall optimization is easy to achieve and an accurate clustering number can be found out.

Description

technical field [0001] The invention belongs to the technical field of image processing and can be used for automatic clustering of images with obvious shape features. Background technique [0002] With the continuous development of science and technology, people's research objects are becoming more and more complex. Vision is one of the main means for human beings to understand and observe the world, and its information is not only huge but also complex. Color, shape, texture and the spatial relationship of things are all components of visual information, which can be used as the focus of research. Shape is one of the most basic characteristics of objects in the sense of visual perception. Research in this area has become an important aspect of computer vision and pattern recognition, and the use of shape features to identify and classify things has also become an important aspect of computer vision and pattern recognition research. one of the main tools. The research on...

AI Technical Summary

Problems solved by technology

However, these methods often have defects: the algorithm often falls into local optimum, and the correct number of clusters cannot be obtained.

Some people use the idea of ​​gradient descent to solve the automatic clustering problem and use it for the clustering of graphics and images, but this method still has the above-mentioned defects. The algorithm is easy to fall into local optimum and cannot get the correct clustering number

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