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Tent mapping improved bee colony algorithm for image threshold segmentation

A technology of threshold segmentation and bee colony algorithm, applied in image analysis, image data processing, calculation and other directions, can solve the problems of slow search speed and inconsistent coding, and achieve the effect of fast search speed

Inactive Publication Date: 2015-09-02
NORTHEAST GASOLINEEUM UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide an improved bee colony algorithm based on Tent mapping for image threshold segmentation, which solves the problems that existing algorithms are prone to premature convergence, non-uniform coding, and slow search speed

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  • Tent mapping improved bee colony algorithm for image threshold segmentation

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Embodiment Construction

[0036] The present invention will be described in detail below in combination with specific embodiments.

[0037] The invention proposes an improved artificial bee colony algorithm by utilizing the mechanism that bees search for honey sources in the basic bee colony algorithm.

[0038] An Improved Bee Colony Algorithm for Tent Mapping

[0039] 1.1 Tent mapping, using Tent mapping to initialize the individual of the algorithm;

[0040] Chaos optimization is a relatively new optimization algorithm. Logistic and Tent are commonly used chaotic sequences in this type of algorithm. The common feature of this type of sequence is that it has initial value sensitivity. So use this feature to initialize the algorithm. Logistic can be described by the following formula:

[0041] z k+1 =μz k (1-z k ); (1)

[0042] The expression form of Tent mapping is:

[0043] z k + 1 = ...

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Abstract

The object of the invention is to provide a Tent mapping improved bee colony algorithm for image threshold segmentation. The method includes: employing Tent mapping to initialize the individuals of the algorithm; employing the Tent mapping to uniformly distribute initial values in a solution space; employing the standard manual bee colony algorithm to search a new nectar source near a nectar source; updating the position of the new nectar source, maintaining the position of the new nectar source if the position is better than the position of the original nectar source, and otherwise maintaining the position of the original nectar source; updating the position of the new nectar source aiming to a following bee according to the method of step 3 and the selection probability of roulette, maintaining the position of the new nectar source if the position is better than the position of the original nectar source, and otherwise maintaining the position of the original nectar source; updating the optimal solution aiming to a leading bee and the following bee; re-generating the nectar source individual if the optimal solution reaches the limited number; transferring to the step 3 for iteration if the iteration frequency is lower than the preset iteration frequency; and otherwise outputting the optimal solution. The beneficial effects of the Tent mapping improved bee colony algorithm are that premature convergence is prevented, and the searching speed is fast.

Description

technical field [0001] The invention belongs to the technical field of target detection algorithms and relates to an improved bee colony algorithm for Tent mapping of image threshold segmentation. Background technique [0002] Image threshold segmentation is a key technology in object detection, and it is widely used because of its high efficiency and easy implementation. A large number of threshold selection methods have been proposed, which are based on one-dimensional histogram or two-dimensional histogram and its area division method [1] , combined with intelligent algorithms to find the optimal threshold under different criteria [2] , and achieved good application results in different application fields. Artificial Bee Colony Algorithm [1] (Artificial Bee Colony, ABC) is a typical algorithm in this kind of intelligent algorithm. This algorithm is a stochastic optimization algorithm based on the intelligent search behavior of bee swarms proposed by Karaboga in 2005. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06T7/00
Inventor 霍凤财董宏丽任伟建路阳王艳芹康朝海于镝张会珍
Owner NORTHEAST GASOLINEEUM UNIV
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