# Construction of a Smart Library Subject Precise Service Platform Based on User Needs

Authors: Yiying Bai
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Under the development and support of digital, networked and intelligent technologies (IT), university wisdom libraries adopt superb IT, sophisticated big data resource allocation, fine supervised wisdom RFD management system and exquisite supervised wisdom service system to provide a wisdom environment to meet users’ personalized needs and promote the transformation of libraries to a new form of wisdom. University intelligent libraries are still in their infancy and development stages in today’s world; the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level, and the level of intelligence has not yet reached a significant level. This paper develops the construction of a precise service platform for wisdom library disciplines based on users’ needs. In this paper, we propose a hybrid continuous ant colony optimization (BACO) method, and further propose a BACO-based multidoor image segmentation bun type for facial image segmentation. The experiments show that the proposed method has good performance and can lay the foundation for the next step of facial recognition and even the construction of smart library to ensure the accurate service of university smart library.


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## Body

## 1. Introduction

A new type of library has emerged, known as a “smart library,” which is comprised of sensors, supercomputers, cloud computing, and Internet of Things (IoT) technology that has fully connected the smart Earth, human civilization, and physical systems. Information Technology (IT) has enabled the development of a new type of library, known as a “smart library.” In addition to offering comprehensive perception, the smart library, which is constructed on top of the digital library, may be able to give intelligent, accurate, and dynamic service and management while remaining flexible in the face of time, place, and geographical limits. In today’s world, university intelligent libraries are still in their infancy and development stages; the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level. University libraries should pay particular attention to the creation of wisdom libraries, optimize and improve the administration and services provided by wisdom libraries, and provide users with a wise, accurate, and human-centered wisdom service and environment that is individualized and ubiquitous [1–6].A definition for the phrase “smart library” was originally offered by Ettola of the Oulu University Library in Finland, who was the first to do so. The term “smart library” was used by Ettola more than 203 years ago to describe “mobile libraries that are not confined by space and can be sensed.” He defined a smart library as “a mobile library with the ability to aid users in locating the documents and materials they seek.” Researchers in China are looking into smart libraries from a variety of perspectives, including perception computing, function and technology, intelligent architectural design, the humanities, and users, to name a few. With a few notable exceptions, scholars’ present opinions and points of view are generally focused on intelligent technology and human challenges, rather than other topics. Considering the concept of smart libraries in terms of modern technology blended with human factors, the author is in agreement with the concept. Based on user needs, a smart library in higher education is a new type of library supported by digital, networked, and IT and that is based on user needs, integrates massive resources, and provides a smart environment and services that meet the growing personalized needs of users, as defined by the Institute of Museum and Library Services (IMLS) [7, 8]. The original digital library is a network-based information resource sharing center that is capable of digital, networked, and virtualized data transmission and storage, and it is a replica of the traditional physical library in terms of functionality. It is intended to increase the terminal perception layer on the very basis of digital library services to improve the perception of libraries, cloud computing technology to realize virtual network storage and resource sharing, multiterminal device connection to realize self-help and convenient retrieval, not only wired mobile services but also network virtual services and physical space services, as well as accurate and professional knowledge service functions, in the current smart library environment [9–14].A smart society is being created as a result of the continual development of information, networking, and intelligence. The ability to recognize the value of data, promote the integration of data and specialized business, and achieve data empowerment has emerged as the most important challenge in the construction of a smart society. Cloud computing devices, perception devices, and intelligent devices are being used in libraries in conjunction with the development of library information resources, and the service mode of libraries is gradually evolving in the direction of intelligence. “Innovate the service mode,” said General Secretary Xi Jinping in a letter sent to retired National Library experts. Because of this, libraries should strive to provide a rich user experience, pay close attention to the interconnections between libraries and users, implement intelligent management and services, better understand user needs through various types of perception technologies and intelligent devices, and provide intelligent services that meet user needs in the age of smart society [15, 16].At now, the development of libraries to the new form of smart libraries has been the industry consensus, and one of the main elements of smart libraries is complete intelligent perception. The performance of image segmentation algorithms can be improved so that face recognition can acquire higher recognition accuracy and be applied to more complex actual library contexts. Among numerous popular segmentation methods, multithreshold segmentation, as a simple and effective segmentation approach, is extensively utilized among image processing in various fields. Common multithreshold segmentation methods take exhaustive search for picture multithreshold segmentation, with long computation time and constraints, so how to identify the optimal threshold quickly and accurately becomes the key to threshold segmentation [17, 18]. In order to obtain high-quality segmentation of Kan values, many researchers have conducted related methodological studies in recent years, and some scholars have developed a hierarchical multilevel segmentation framework to improve the accuracy and robustness of segmentation to voice.We propose a variant algorithm of the successive ant colony optimization algorithm (BACO) is proposed in this paper, to demonstrate that the BACO can obtain high-quality segmentation results, based on four facial images from the Berkeley 500 (BSDS50°), this paper combines the BACO and three other similar methods are compared in experiments at high and low threshold levels, and the results are evaluated using peak signal-to-noise ratio (PSNR). This paper also analyzes and compares the evaluation results of PSNR in detail using Wicoxon signed-rank test. The results show that the BACO method is a multithreshold image segmentation method that can obtain high-quality image segmentation results [4, 19, 20].In today’s world, the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level, and university intelligent libraries are still in their infancy and development stages. At now, the development of libraries to the new form of smart libraries has been the industry consensus, and one of the main elements of smart libraries is complete intelligent perception.The paper arrangements are as follows:Section2 discusses the Evolution of Smart Libraries and the connotation and evolution of the next-generation library service platform. The system examines the Development of Intelligent Libraries and smart library possesses the characteristics of total perception, vast interconnection, deep integration, and accessibility. Section 3 defines the various methods to select the data processing. Section 4 determines the experiments and concludes the results. Section 5 concludes the article.


## 2. The Evolution of Smart Libraries

In this section, define the connotation and evolution of the next-generation library service and study the previous generation of library automation systems. During the first few years of development and natural market selection, several international next-generation library service platforms achieved major advances forward in their development. The development of intelligent library systems has been discussed.


### 2.1. The Connotation and Evolution of the Next-Generation Library Service Platform

Marshall breeding presented a new generation of library automation system in the Automation Markelplace annual report, which was published in 2011. The system was accepted. 2012 marked the first time that the term “library service platform” was used in a formal annual report, marking the transition from the previous generation of library automation systems to the new generation of library service platforms stage. So the library system has evolved through three stages (see Table1): from manual card catalog to single-module computer application with machine-readable MARC markings, from single-module to multimodule function transformation, from paper book management system to paper and electricity integrated whole life cycle management, resource discovery and access, and finally to the next generation of library service platform (see Figure 1).Table 1
Library system development stages (in years).
First generationSecond generationThird generationTime1950∼19701971∼20102011∼ArchitectureSingle module productsMultimodule system integrationService oriented architecture, microservice architectureFunctional featuresPaperback cataloging systemFull flow management of paper books and journals, OPAC user query interfaceIntegrated management of paper and electricity, unified search and discovery, full lifecycle management of resourcesRepresentative productsOCLC online catalogingInnopac, innovative, dynixWMS, sierra, alma, FOLIOFigure 1
File bag with (k) solutions.The advancement of theory is reliant on the expansion of practical application. ExLibris launched the AImna library services platform in 2011, and it operates on the SaaS service model with a multitenant architecture, according to the company. It consists of the AP1 application and the Primo VE knowledge cart, which is used to discover resources. OCLC launched and deployed WMS (Web-based Management Services) in April of this year. 2012 As a result, companies such as Kuali OLE, Pro Quest, and others have created and launched their own library service platform products, resulting in a major increase in the size of the library service platform industry. With the FOL Oo grant program, which was formed by EBSCO at the end of last year and which comprises librarians, vendors, and developers, EBSCO hopes to encourage the use of opensource technology in libraries and other organizations. A grant program called FOLIO was introduced by EBSCO at the end of 2015 in order to further the effort to develop a microservices architecture-based library services platform. To achieve this purpose, the effort takes use of opensource technology as well as a collaborative community of libraries, vendors, and developers to achieve success.According to the authors, several overseas next-generation library service platforms have made significant strides forward in their development during the course of the first few years of development and natural market selection. Because to the acquisition of ExLibis (2015), pro Quest was able to provide assistance for Alma, which has been extensively embraced by academic libraries, with 2,037 Alma installations as of 2020. The acquisition of inventiveness Polaris Library’s next-generation library services platform has garnered significant adoption among academic and public libraries a year after the company completed the acquisitions of Polaris Library Systems and VTLS, according to the company. ProQuest acquired Innovative in early 2020 and continued to operate it as a distinct business entity in partnership with ExLiris until the end of the fiscal year in question. A substantial amount of success has been accomplished by the OCLC World Metadata System (WMS), which has increased the total number of libraries covered by the WMS to 632 by 2020. FOLIO has been developed and promoted since Plumx was acquired by EBSCO in 2015, and it is now available for purchase. Its electronic resource management component was the first of its kind to be used by a number of library users in Italy, and it continues to be used today. A number of Italian libraries used FOLIO, which was hosted and maintained by EBSCO and launched in 2020 with early subscribers including Chalmers University of Technology in Sweden, the United States, and a number of other libraries in Italy.While this is going on, some university libraries are embarking on a journey of their own, conducting independent research and developing next-generation service platforms for their patrons. Following the adoption of microservice architecture by the Shenzhen University Library in 2016, the development of a unified technique, principles, and practical framework for rebuilding library business systems in accordance with microservice architecture has occurred. The link between FOLIO, the next-generation service platform built by Shenzhen University Library, and the system has been completed in order for the system to be fully integrated.


### 2.2. The Development of Intelligent Libraries

Having been coined by Aittola and colleagues in 2003, the term “smart library” has expanded rapidly as a result of a wave of “knowledge” that has coincided with the creation of notions such as “smart Earth.” Domestic and international research, among other things, has primarily defined the notion of smart library from the perspectives of technology, system function, spatial composition, elements, and services, as well as other aspects.When it comes to the question of “what is a smart library” and “how to construct a smart library,” scholars in the United States and elsewhere have mostly focused on two perspectives. Internet of Things (IoT) and other sensing technologies, for example, can provide users with smart services and administration. Another example is the provision of clients with perceptive mobile library services through the Internet, regardless of time and space constraints. Among the applications are RFID, a “one-stop” search service, a perception service based on semantic technology and social network technology, a mobile digital library, data mining, and a variety of others. Another perspective is system platform construction, which involves the development of a smart library system platform as well as the formation of a smart library service system or service architecture through the use of both hardware and software components. The Ningbo Library proposes a seven-layer architecture for the wisdom library system platform; the Chongqing University Library collaborates with Vipshop in order to adopt Vipshop’s wisdom library data service platform; and the construction and transformation of physical venues, as well as the construction of the wisdom information service platform system and the construction of three aspects of intelligent services, are all part of Nanjing University’s wisdom library progamme; and It is possible to undertake theoretical research in real time throughout the entire field. The author of this paper synthesizes the points of view of all parties, applies the principle of system theory, and proposes that the smart library is based on devices and technologies such as the Internet of Things, big data, block chain, and intelligent computing, and that it integrates specialized management of the library with intelligent perception and computing, and provides users with the required literature, information, data, and other resources effectively, precisely, and quickly, an example of which is the Chinese government’s smart library.It is a library that, in terms of its design, is a natural synthesis of fact and imaginative thought. A smart library possesses the characteristics of total perception, vast interconnection, deep integration, and accessibility, to name a few characteristics.


## 2.1. The Connotation and Evolution of the Next-Generation Library Service Platform

Marshall breeding presented a new generation of library automation system in the Automation Markelplace annual report, which was published in 2011. The system was accepted. 2012 marked the first time that the term “library service platform” was used in a formal annual report, marking the transition from the previous generation of library automation systems to the new generation of library service platforms stage. So the library system has evolved through three stages (see Table1): from manual card catalog to single-module computer application with machine-readable MARC markings, from single-module to multimodule function transformation, from paper book management system to paper and electricity integrated whole life cycle management, resource discovery and access, and finally to the next generation of library service platform (see Figure 1).Table 1
Library system development stages (in years).
First generationSecond generationThird generationTime1950∼19701971∼20102011∼ArchitectureSingle module productsMultimodule system integrationService oriented architecture, microservice architectureFunctional featuresPaperback cataloging systemFull flow management of paper books and journals, OPAC user query interfaceIntegrated management of paper and electricity, unified search and discovery, full lifecycle management of resourcesRepresentative productsOCLC online catalogingInnopac, innovative, dynixWMS, sierra, alma, FOLIOFigure 1
File bag with (k) solutions.The advancement of theory is reliant on the expansion of practical application. ExLibris launched the AImna library services platform in 2011, and it operates on the SaaS service model with a multitenant architecture, according to the company. It consists of the AP1 application and the Primo VE knowledge cart, which is used to discover resources. OCLC launched and deployed WMS (Web-based Management Services) in April of this year. 2012 As a result, companies such as Kuali OLE, Pro Quest, and others have created and launched their own library service platform products, resulting in a major increase in the size of the library service platform industry. With the FOL Oo grant program, which was formed by EBSCO at the end of last year and which comprises librarians, vendors, and developers, EBSCO hopes to encourage the use of opensource technology in libraries and other organizations. A grant program called FOLIO was introduced by EBSCO at the end of 2015 in order to further the effort to develop a microservices architecture-based library services platform. To achieve this purpose, the effort takes use of opensource technology as well as a collaborative community of libraries, vendors, and developers to achieve success.According to the authors, several overseas next-generation library service platforms have made significant strides forward in their development during the course of the first few years of development and natural market selection. Because to the acquisition of ExLibis (2015), pro Quest was able to provide assistance for Alma, which has been extensively embraced by academic libraries, with 2,037 Alma installations as of 2020. The acquisition of inventiveness Polaris Library’s next-generation library services platform has garnered significant adoption among academic and public libraries a year after the company completed the acquisitions of Polaris Library Systems and VTLS, according to the company. ProQuest acquired Innovative in early 2020 and continued to operate it as a distinct business entity in partnership with ExLiris until the end of the fiscal year in question. A substantial amount of success has been accomplished by the OCLC World Metadata System (WMS), which has increased the total number of libraries covered by the WMS to 632 by 2020. FOLIO has been developed and promoted since Plumx was acquired by EBSCO in 2015, and it is now available for purchase. Its electronic resource management component was the first of its kind to be used by a number of library users in Italy, and it continues to be used today. A number of Italian libraries used FOLIO, which was hosted and maintained by EBSCO and launched in 2020 with early subscribers including Chalmers University of Technology in Sweden, the United States, and a number of other libraries in Italy.While this is going on, some university libraries are embarking on a journey of their own, conducting independent research and developing next-generation service platforms for their patrons. Following the adoption of microservice architecture by the Shenzhen University Library in 2016, the development of a unified technique, principles, and practical framework for rebuilding library business systems in accordance with microservice architecture has occurred. The link between FOLIO, the next-generation service platform built by Shenzhen University Library, and the system has been completed in order for the system to be fully integrated.


## 2.2. The Development of Intelligent Libraries

Having been coined by Aittola and colleagues in 2003, the term “smart library” has expanded rapidly as a result of a wave of “knowledge” that has coincided with the creation of notions such as “smart Earth.” Domestic and international research, among other things, has primarily defined the notion of smart library from the perspectives of technology, system function, spatial composition, elements, and services, as well as other aspects.When it comes to the question of “what is a smart library” and “how to construct a smart library,” scholars in the United States and elsewhere have mostly focused on two perspectives. Internet of Things (IoT) and other sensing technologies, for example, can provide users with smart services and administration. Another example is the provision of clients with perceptive mobile library services through the Internet, regardless of time and space constraints. Among the applications are RFID, a “one-stop” search service, a perception service based on semantic technology and social network technology, a mobile digital library, data mining, and a variety of others. Another perspective is system platform construction, which involves the development of a smart library system platform as well as the formation of a smart library service system or service architecture through the use of both hardware and software components. The Ningbo Library proposes a seven-layer architecture for the wisdom library system platform; the Chongqing University Library collaborates with Vipshop in order to adopt Vipshop’s wisdom library data service platform; and the construction and transformation of physical venues, as well as the construction of the wisdom information service platform system and the construction of three aspects of intelligent services, are all part of Nanjing University’s wisdom library progamme; and It is possible to undertake theoretical research in real time throughout the entire field. The author of this paper synthesizes the points of view of all parties, applies the principle of system theory, and proposes that the smart library is based on devices and technologies such as the Internet of Things, big data, block chain, and intelligent computing, and that it integrates specialized management of the library with intelligent perception and computing, and provides users with the required literature, information, data, and other resources effectively, precisely, and quickly, an example of which is the Chinese government’s smart library.It is a library that, in terms of its design, is a natural synthesis of fact and imaginative thought. A smart library possesses the characteristics of total perception, vast interconnection, deep integration, and accessibility, to name a few characteristics.


## 3. Method

### 3.1. Mixed Continuous Ant Colony Optimization Algorithm

The continuous ant colony optimization algorithm is an improved version of the ant colony algorithm proposed by Shu et al. [7]. That allows the ACO algorithm to be used for more than just solving discrete problems. The core of ACOR is applied to update the pheromone in the algorithm by converting a discrete pdf into a continuous pdf while using a portion of the solutions to form a solution archive.The Gaussian kernel functionGix is used by ACOR, and Gix is weighted together with a one-dimensional Gaussian function to obtain(1)Gix=∑l=1kωlglix,=∑l=1kωlglixωl1σli2πe−x−μli2/2σli2,where ω=ω1,ω2,⋯ωk is the weight vector, μ=μ1,μ2,⋯μk male is the mean vector, σ=σ1,σ2,⋯σk is the standard deviation vector.The solutions are saved with the objective function value and the relevant weight of each solution as part of the process of creating the solution profile. In Figure1, the quality of the solutions to the associated objective function is decided by the ranking of the solutions, with the higher quality solutions being ranked higher and the lower quality answers being ranked lower.Assuming that the size of the function value of each solution satisfiesfx1≤⋯fxl≤⋯fxk, the weights ωl of each solution can be correspondingly induced to satisfy ω1≥⋯ωl≥⋯ωk for(2)ωl=1qk2πe−l−12/2q2k2,where q is a parameter.They collect the variousk items and apply the function and choose the k weight function. In the process of selection it is first necessary to calculate the weight size ωi, choose the Gaussian function glix according to the probability pl, and determine the guided solution sl, subsequently each position vector around the guided solution sl, is selected by the Gaussian function. pl,μli,σli Can be obtained by, and equations (3)–(5):(3)pl=ωl∑r=1kωr,(4)μi=μ1i,⋯,μki=s1i,⋯,ski,(5)σli=ξ∑e=1ksei−slik−1,where ξ value is a parameter and ξ>0.In the original ACOR, the core is that some of the better solutions guide other solutions to search for better solutions in the solution space. However, the optimal solution individuals update themselves fixedly and lack the ability to exchange information with other individuals and learn independently. Therefore, it is difficult to update the entire solution space effectively during the iterative process, which leads to a local optimum. Therefore, in order to prevent the ACOR algorithm from falling into a local optimum as much as possible and to overcome the disadvantage of inefficient development of the algorithm in later stages, a swarm-assisted foraging strategy based on the artificial bee colony algorithm (ABC) is proposed in this paper. The strategy is mainly divided into three stages, and the details are as follows.In the first stage, after each ant finds a food location and evaluates the weights, it generates new candidate solutions based on its own previous searched solutions through equation (6) based on a search around that food location.(6)vi,j=xi,j+ϕi,jxi,j−xk,j,where k∈1,2,⋯,N,j∈1,2,⋯,D denotes the selected food location to be updated.In the first peal stage, each individual ant informs the other ants about the food location, and then the other ants calculate the probability of being selected for the relevant food locationpi based on the obtained information:(7)pi=fiti∑j=1Nfiti,where: pi denotes the probability of being selected for food position xi, and fiti denotes the adaptation value of the food position. It can be shown that the larger the fit value is, the greater the likelihood that the matching meal position will be selected in the future. As a result, the meal location is determined by a game of roulette, and a new candidate solution vi is formed for the selected individual by utilizing equation (6). If the fitness value of vi is better than xi, then it is replaced, otherwise it remains unchanged.In the third stage, the algorithm model selects the food location that has not been updated for the longest period of time, and if the number of times it has not been updated exceeds a predetermined limit value, a new food location is randomly generated to replace the food location that was selected in the previous stage.It is proposed in this study that an enhanced ACOR, referred to as BACO, be used to address the problem that the search ability of ACOR is not strong and the convergence speed of ACOR is too sluggish. In ACOR, the foraging ability of ants is effectively increased by applying the swarm-assisted foraging strategy to the end stage of the ant foraging process. This means that the merit-seeking ability of the late iteration of the ACOR algorithm is strengthened, and the convergence speed of the algorithm is increased as a result.The swarm-assisted foraging strategy not only improves the optimization search effect in the late stages of the algorithm, but it also improves the communication learning ability of individual ants throughout the entire iteration, which means that the effectiveness of ACOR in updating the solution during the iteration is improved, thereby improving the algorithm’s ability to jump out of the local optimum.


### 3.2. Multithreshold Image Segmentation Model

Nonlocal averaging can remove the “noise” from an image by using all the pixels in the image. The principle of nonlocal mean is to obtain a more accurate image after removing noise by weighting the pixels according to some similarity. The similarity between pixels is calculated using the Euclidean distance between rectangular neighborhoods of pixels, which can be described as a block of image structural features looking for similar blocks in the whole image. It is assumed that I (p) and 1 (q) are the gray scale values of pixel points p and q in the image. The non-local mean values of the image are obtained by equations (8)–(11):(8)Op=∑q∈IIqωp,q∑q∈Iωp,q,(9)ωp,q=exp−μp−μq/σ2,(10)μp=1m×m∑i∈LpIi,(11)μp=1m×m∑i∈LqIi,where ωp,q is derived from the similarity between the pixel neighborhoods p and q. μp−μq2 Is the Euclidean distance of the rectangular neighborhood between p and q. σ Is called the smoothing parameter, the size of σ controls the degree of noise removal of the image, the larger the σ higher the level of noise removal of the image, the more blurred the image, the smaller the σ lower the level of noise removal of the image, the more noise the image contains. m×m Is the size of the rectangular pixel neighborhood window, p (p) and p (q) are the gray scale values of the pixels in the gray scale image. μp And μq are the gray scale values of the pixels in the gray scale image.Suppose the size of the image isM×N, p∈0,M and q∈0,N. After obtaining the gray value vp,q and the non-local mean μp,q of the vp,q∈0,L−1 and μp,q∈0,L−1, a two-dimensional histogram can be built image pixels,(12)Pij=hijM×N,where i is the value of pixel gray value vp,q, j is the value of non-local mean μp,q, hij is the two-dimensional level of a pixel point at gray value i and non-local mean j, Pij is the normalized two-dimensional histogram (Oxyz) in the z-axis.Based on the 2D histogram generated from the image, the BACO algorithm in this paper calculates the 2D histogram, which can describe the information in the image more completely using the image gray value and the image nonlocal mean value, and is not as sensitive to the effect of image noise as the 1D histogram.For the purpose of picture segmentation, we employ the characteristics of a swarm intelligence optimization method to optimize the threshold level in numerous iterations, resulting in the desired effect. In the image segmentation process, the color image is first grayed to create a gray value image; the gray value of each pixel in the image is then used to generate the nonlocal mean image after removing the noise; the pixel gray value and the nonlocal mean information are then used to generate the 2D histogram, with theZ-axis of the 2D histogram representing the normalized data; and the gray value of each pixel in the image is then used to generate the 2D his This is accomplished by normalizing the Z-axis data and then generating an appropriate threshold by iterative optimization of the BACO optimization method, utilizing 2D Kapur first as the information metric for the BACO optimization process. The following are the stages involved in developing the BACO-based multithreshold picture segmentation model:Step 1: input the original facial imageStep 2: generate gray scale image and nonlocal mean imageStep 3: construct a 2D histogram based on the results of Step 2Step 4: find the optimal set of thresholds using BACO with 2D Kapur first as the objective functionStep 5: segment the gray scale image with the obtained optimal set of thresholdsStep 6: output the image segmentation results


## 3.1. Mixed Continuous Ant Colony Optimization Algorithm

The continuous ant colony optimization algorithm is an improved version of the ant colony algorithm proposed by Shu et al. [7]. That allows the ACO algorithm to be used for more than just solving discrete problems. The core of ACOR is applied to update the pheromone in the algorithm by converting a discrete pdf into a continuous pdf while using a portion of the solutions to form a solution archive.The Gaussian kernel functionGix is used by ACOR, and Gix is weighted together with a one-dimensional Gaussian function to obtain(1)Gix=∑l=1kωlglix,=∑l=1kωlglixωl1σli2πe−x−μli2/2σli2,where ω=ω1,ω2,⋯ωk is the weight vector, μ=μ1,μ2,⋯μk male is the mean vector, σ=σ1,σ2,⋯σk is the standard deviation vector.The solutions are saved with the objective function value and the relevant weight of each solution as part of the process of creating the solution profile. In Figure1, the quality of the solutions to the associated objective function is decided by the ranking of the solutions, with the higher quality solutions being ranked higher and the lower quality answers being ranked lower.Assuming that the size of the function value of each solution satisfiesfx1≤⋯fxl≤⋯fxk, the weights ωl of each solution can be correspondingly induced to satisfy ω1≥⋯ωl≥⋯ωk for(2)ωl=1qk2πe−l−12/2q2k2,where q is a parameter.They collect the variousk items and apply the function and choose the k weight function. In the process of selection it is first necessary to calculate the weight size ωi, choose the Gaussian function glix according to the probability pl, and determine the guided solution sl, subsequently each position vector around the guided solution sl, is selected by the Gaussian function. pl,μli,σli Can be obtained by, and equations (3)–(5):(3)pl=ωl∑r=1kωr,(4)μi=μ1i,⋯,μki=s1i,⋯,ski,(5)σli=ξ∑e=1ksei−slik−1,where ξ value is a parameter and ξ>0.In the original ACOR, the core is that some of the better solutions guide other solutions to search for better solutions in the solution space. However, the optimal solution individuals update themselves fixedly and lack the ability to exchange information with other individuals and learn independently. Therefore, it is difficult to update the entire solution space effectively during the iterative process, which leads to a local optimum. Therefore, in order to prevent the ACOR algorithm from falling into a local optimum as much as possible and to overcome the disadvantage of inefficient development of the algorithm in later stages, a swarm-assisted foraging strategy based on the artificial bee colony algorithm (ABC) is proposed in this paper. The strategy is mainly divided into three stages, and the details are as follows.In the first stage, after each ant finds a food location and evaluates the weights, it generates new candidate solutions based on its own previous searched solutions through equation (6) based on a search around that food location.(6)vi,j=xi,j+ϕi,jxi,j−xk,j,where k∈1,2,⋯,N,j∈1,2,⋯,D denotes the selected food location to be updated.In the first peal stage, each individual ant informs the other ants about the food location, and then the other ants calculate the probability of being selected for the relevant food locationpi based on the obtained information:(7)pi=fiti∑j=1Nfiti,where: pi denotes the probability of being selected for food position xi, and fiti denotes the adaptation value of the food position. It can be shown that the larger the fit value is, the greater the likelihood that the matching meal position will be selected in the future. As a result, the meal location is determined by a game of roulette, and a new candidate solution vi is formed for the selected individual by utilizing equation (6). If the fitness value of vi is better than xi, then it is replaced, otherwise it remains unchanged.In the third stage, the algorithm model selects the food location that has not been updated for the longest period of time, and if the number of times it has not been updated exceeds a predetermined limit value, a new food location is randomly generated to replace the food location that was selected in the previous stage.It is proposed in this study that an enhanced ACOR, referred to as BACO, be used to address the problem that the search ability of ACOR is not strong and the convergence speed of ACOR is too sluggish. In ACOR, the foraging ability of ants is effectively increased by applying the swarm-assisted foraging strategy to the end stage of the ant foraging process. This means that the merit-seeking ability of the late iteration of the ACOR algorithm is strengthened, and the convergence speed of the algorithm is increased as a result.The swarm-assisted foraging strategy not only improves the optimization search effect in the late stages of the algorithm, but it also improves the communication learning ability of individual ants throughout the entire iteration, which means that the effectiveness of ACOR in updating the solution during the iteration is improved, thereby improving the algorithm’s ability to jump out of the local optimum.


## 3.2. Multithreshold Image Segmentation Model

Nonlocal averaging can remove the “noise” from an image by using all the pixels in the image. The principle of nonlocal mean is to obtain a more accurate image after removing noise by weighting the pixels according to some similarity. The similarity between pixels is calculated using the Euclidean distance between rectangular neighborhoods of pixels, which can be described as a block of image structural features looking for similar blocks in the whole image. It is assumed that I (p) and 1 (q) are the gray scale values of pixel points p and q in the image. The non-local mean values of the image are obtained by equations (8)–(11):(8)Op=∑q∈IIqωp,q∑q∈Iωp,q,(9)ωp,q=exp−μp−μq/σ2,(10)μp=1m×m∑i∈LpIi,(11)μp=1m×m∑i∈LqIi,where ωp,q is derived from the similarity between the pixel neighborhoods p and q. μp−μq2 Is the Euclidean distance of the rectangular neighborhood between p and q. σ Is called the smoothing parameter, the size of σ controls the degree of noise removal of the image, the larger the σ higher the level of noise removal of the image, the more blurred the image, the smaller the σ lower the level of noise removal of the image, the more noise the image contains. m×m Is the size of the rectangular pixel neighborhood window, p (p) and p (q) are the gray scale values of the pixels in the gray scale image. μp And μq are the gray scale values of the pixels in the gray scale image.Suppose the size of the image isM×N, p∈0,M and q∈0,N. After obtaining the gray value vp,q and the non-local mean μp,q of the vp,q∈0,L−1 and μp,q∈0,L−1, a two-dimensional histogram can be built image pixels,(12)Pij=hijM×N,where i is the value of pixel gray value vp,q, j is the value of non-local mean μp,q, hij is the two-dimensional level of a pixel point at gray value i and non-local mean j, Pij is the normalized two-dimensional histogram (Oxyz) in the z-axis.Based on the 2D histogram generated from the image, the BACO algorithm in this paper calculates the 2D histogram, which can describe the information in the image more completely using the image gray value and the image nonlocal mean value, and is not as sensitive to the effect of image noise as the 1D histogram.For the purpose of picture segmentation, we employ the characteristics of a swarm intelligence optimization method to optimize the threshold level in numerous iterations, resulting in the desired effect. In the image segmentation process, the color image is first grayed to create a gray value image; the gray value of each pixel in the image is then used to generate the nonlocal mean image after removing the noise; the pixel gray value and the nonlocal mean information are then used to generate the 2D histogram, with theZ-axis of the 2D histogram representing the normalized data; and the gray value of each pixel in the image is then used to generate the 2D his This is accomplished by normalizing the Z-axis data and then generating an appropriate threshold by iterative optimization of the BACO optimization method, utilizing 2D Kapur first as the information metric for the BACO optimization process. The following are the stages involved in developing the BACO-based multithreshold picture segmentation model:Step 1: input the original facial imageStep 2: generate gray scale image and nonlocal mean imageStep 3: construct a 2D histogram based on the results of Step 2Step 4: find the optimal set of thresholds using BACO with 2D Kapur first as the objective functionStep 5: segment the gray scale image with the obtained optimal set of thresholdsStep 6: output the image segmentation results


## 4. Experiments and Results Analysis

In order to demonstrate that the BACO method can obtain high-quality segmentation results, this paper uses four facial images of BSDS500 to compare the BACO method with similar methods at both high and low threshold levels. The four images involved in the experiments are given in Figure2.Figure 2
Images for segmentation. (a–d) Rae image and 2D histograms.
(a)(b)(c)(d)It was necessary to conduct segmentation comparison experiments for six threshold levels: 3, 4, 5, 13, 14, and 15, in order to ensure the fairness of the experiments and the reliability of the experimental results. The low-dimensional threshold levels (3, 4, and 5) were represented by the low-dimensional threshold levels and the high-dimensional threshold levels (13, 14 and 15) were represented by the high-dimensional threshold levels (13, 14 and 15). We conducted all of our segmentation studies in an iterative fashion, with a total of 100 iterations. It was decided that the size of the selected segmented images would be 321–481 pixels, and the population size of the optimization algorithm in all of the segmentation methods involved in the comparison was set to 20 pixels. All experiments were run independently for a total of 30 times in order to avoid any randomness in the experiments. For the purpose of getting experimental data, the performance of PSNR and FSIM were examined in this article, and the evaluation results of PSNR were thoroughly analyzed and compared using the Wilcoxon signed-rank test.We compared the differences between the algorithm in this paper and the CS algorithm in terms of PSNR metrics, and it can be seen that the algorithm in this paper significantly outperforms the PSNR. Comparison of the method with CS is shown in Figure3.Figure 3
Comparison of our method with CS.The precise conclusions obtained by each approach at a threshold level of 15 for the image in Figure2(b) are presented in Figure 4 for the image in Figure 2(a). It has been determined that, when compared to the other methods, the results obtained by the BACO-based multithreshold image segmentation method are not only the best in terms of feature retention, but they are also the best in terms of overall segmentation effect. This conclusion is based on careful observation and comparison of the segmentation results obtained by each method. Therefore, a review of the aforementioned data reveals that the BACO-based multithreshold image segmentation method is a multithreshold image segmentation method capable of delivering high-quality image segmentation results, as demonstrated in the previous section.Figure 4
Gray segmentation results and color mapping results of Figure2(b) obtained by each method at the threshold level of 15.


## 5. Conclusion

The constant improvement of intelligent technology has contributed to the improvement of the intelligent level of university intelligent libraries. To keep up with the times, the university library should be responsive to changes in technology and user needs, be perceptive in all areas, and expedite the construction and transformation of the library’s intelligent equipment and space service management systems. It is proposed in this paper that, in order to improve the precise service of smart library disciplines, humanization, and personalization, as well as create a smart digital service space and promote the creation of smart campuses and innovative activities, we should use a group intelligence optimization method with the ability to obtain high-quality threshold sets, which we shall refer to as BACO. Second, using approaches such as nonlocal mean and Kapur entropy, this paper develops a novel multi thresholding image segmentation model that is based on the suggested BACO and incorporates it. The results demonstrate that the BACO-based multithreshold image segmentation method can produce high-quality segmentation results, which can be used to improve the accuracy of smart library services, increase the innovation and creativity of users, and make greater contributions to the construction and development of university teaching and research facilities and infrastructure.



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*Source: 5675291-2022-04-06.xml*