Improved cat swarm algorithm based target extraction and classification method

Abstract

The invention provides an improved cat swarm algorithm based target extraction and classification method. A conventional swarm intelligence algorithm is excessively high in complexity of target extraction and classification, and easily falls into local optimum to cause ''precocity''. For example, a cat swarm algorithm is a typical swarm intelligence algorithm but has the shortcomings of excessively long running time and low accuracy in big data image processing. For the deficiencies of the cat swarm algorithm, the invention provides an improved cat swarm algorithm, an inertial weight coefficient and an acceleration coefficient are added in a tracking mode of the algorithm, so that the running speed of the algorithm is increased and the running time is shortened. Moreover, the improved cat swarm algorithm is applied to target object extraction and classification, namely, the method comprises: firstly, inputting images; preprocessing the images; thresholding the images into binary images, and extracting an interested target image; calculating four features of the target image to form new feature vectors; and finally, performing classification by applying the improved cat swarm algorithm. The method can not only increase the calculation speed but also improve the accuracy of target object extraction and classification.

Description

technical field [0001] The invention relates to swarm intelligence, bionic computing and pattern recognition technology, in particular to a method for object extraction and classification based on the improved cat group algorithm. This method has broad application prospects in image recognition, pattern classification, object tracking and other fields. Background technique [0002] The hotspots and difficulties of target object extraction and classification based on clustering methods in the fields of image processing and pattern recognition. Therefore, many scholars devote themselves to researching this hot spot and difficulty, and propose a series of clustering algorithms at the same time. For example, the well-known k-means algorithm (Terada Yoshikazu. Strong Consistency of Reduced K-means Clustering [J]. Scandinavian journal of statistics, 41 (4), 2014) and other traditional clustering algorithms. However, this algorithm has a big disadvantage, that is, it is sensitive...

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Problems solved by technology

However, these algorithms have their own shortcomings. For example, the ant colony algorithm introduces pheromone, which increases the time complexity of the algorithm, requires a long search time, and is prone to stagnation during the search process; In the later stage, it may not be able to jump out of the local optimum; although the cat swarm algorithm can achieve good results in function optimization, it has problems of slow speed and too long time in image processing

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