Image retrieval method based on memetic algorithm

A graphics and algorithm technology, applied in the computer field, can solve problems such as less algorithm research, achieve fast convergence, good retrieval results, and enhance global search capabilities

Inactive Publication Date: 2012-07-18
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0028] At present, the traditional image retrieval method is to study the similarity difference between ...

Method used

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  • Image retrieval method based on memetic algorithm
  • Image retrieval method based on memetic algorithm
  • Image retrieval method based on memetic algorithm

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0100] The present invention is a graphic retrieval method based on a cryptographic algorithm, which can be used for retrieval and classification of a large number of pictures in the network, mainly for retrieval of images with clear outlines, and the working platform is a software operating environment, refer to figure 1 , the present invention comprises the following steps:

[0101] Step 1: Manually set parameters: including: the maximum number of iterations of the program running t max , the number of retrieved graphics n, the mutation probability p m ∈[0,1], the maximum number of categories k max , the number of antibodies S, the antibody replication factor N c , the initial temperature T of the simulated annealing algorithm 0 , annealing coefficient d, define the affinity value of antibody A Where α is a constant, Ncluster is the number of categories C represented by antibody A decoding, dist(C i ) is the class C represented by antibody A decoding i The sum of simi...

Embodiment 2

[0160] Simulation

[0161] The graphic retrieval method based on secret matrix algorithm is the same as embodiment 1, and the effect of the present invention is further illustrated by following experiments:

[0162] 1. Parameter setting conditions of the simulation experiment:

[0163] Manually input related parameters: population size: S=30, mutation probability: P m = 0.3, diversity control parameter for clonal selection: β = 0.3, clonal size factor: N c = 85, the largest running algebra: t max =100, the maximum and minimum values ​​of the number of clusters: k max =15, the parameter α is set differently for each set of retrieved graphics.

[0164] 2. Simulation experiment environment:

[0165] The CPU is core2 2.4HZ, the memory is 2G, and the WINDOWS XP system is simulated using MATLAB 7.0.

[0166] 3. Simulation content

[0167] (1) Kimia-25 graphics set

[0168] The Kimia-25 graphics set contains 25 graphics, which belong to 6 categories, each row belongs to one c...

Embodiment 3

[0174] The graphic retrieval method based on the dense mother algorithm is the same as embodiment 1-2, in conjunction with the appended image 3 , to introduce the specific process of the recombination operation: select two antibodies individual1 and individual2, select class label 2 in individual1, then the positions with class label 2 are 2, 5, 6, 7, 10, 11 respectively, which are recorded as set1. In the antibody individual2, find the class labels corresponding to the position of set1, which are 1, 3, 3, 1, 3, and 3, respectively. The maximum number of the same class marks is 3, and the corresponding positions are 5, 6, 10, and 11. In individual2, find the position labeled 3 as set2. In antibody individual1, the class label of the position corresponding to set2 is changed to 2, and the class label of the position corresponding to set1 in antibody individual2 is changed to 3.

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Abstract

The invention discloses an image retrieval method based on memetic algorithm, and relates to shape-based image retrieval. The image retrieval method comprises setting parameters; generating an initial population; calculating antibody affinity; cloning; executing clonal variation based on probability; performing clonal selection; recombining; subjecting the antibody to local search operator optimization based on simulated annealing algorithm; optimizing superior antibodies by using a local search operator (1) and a local search operator (2); and repeating the operations to realize rapid and effective image retrieval. By combining the clonal selection algorithm with the local search operators, the image retrieval method provided by the invention has high global search capacity, high convergence rate and high image retrieval efficiency. The local search operators with high local search capacity can further improve the retrieval result of the clonal selection algorithm so as to improve the accuracy of the image retrieval results. In addition, the method can overcome the difficulty in determining the number of classes by using a coding method based on class marks. Based on the advantages of high efficiency and high accuracy, the image retrieval method provided by the invention can be used for retrieving and classifying network pictures.

Description

technical field [0001] The invention belongs to the field of computers, and further relates to a shape-based graphic retrieval method, in particular to a cryptographic algorithm-based graphic retrieval method, which can be used for retrieval and classification of a large number of pictures in the network. Background technique [0002] With the rapid development of the Internet, the multimedia information on the Internet has also increased dramatically, so people's demand for multimedia information retrieval has also followed. Traditional information retrieval mainly focuses on text retrieval, and there are not many researches on multimedia. The multimedia on the Internet is mainly based on images, so image retrieval has become a hot research topic at present. Both military and civilian installations generate gigabytes of imagery every day. These digital images contain a lot of useful information. However, since these images are randomly distributed all over the world, the...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06T7/00
Inventor 刘若辰唐丽娜焦李成李阳阳公茂果马文萍王爽朱虎明
Owner XIDIAN UNIV
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