Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494392
A. Wilbik, P. Grefen
The abundant availability of data allows the construction of predictive systems that support decision makers in business and society. A problem arises if an organization does not have a large enough data set by itself to construct a system of adequate quality. In this case, data across organizations has to be used, which introduces risks of data sharing. To overcome these risks, federated learning is getting increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. So far, only crisp systems have been used in this context. The use of a fuzzy inference system can bring advantages to deal with vagueness and uncertainty in predictive systems. Therefore, in this paper we explore the (hopefully) happy marriage of federated learning and fuzzy inference mechanisms. We show that it is indeed possible to build a fuzzy inference model in a federated learning setting, resulting in a Federated Fuzzy Learning System (F2LS). We also show that this combination brings advantages to decision making that cannot be achieved with either mechanism in isolation.
{"title":"Towards a Federated Fuzzy Learning System","authors":"A. Wilbik, P. Grefen","doi":"10.1109/FUZZ45933.2021.9494392","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494392","url":null,"abstract":"The abundant availability of data allows the construction of predictive systems that support decision makers in business and society. A problem arises if an organization does not have a large enough data set by itself to construct a system of adequate quality. In this case, data across organizations has to be used, which introduces risks of data sharing. To overcome these risks, federated learning is getting increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. So far, only crisp systems have been used in this context. The use of a fuzzy inference system can bring advantages to deal with vagueness and uncertainty in predictive systems. Therefore, in this paper we explore the (hopefully) happy marriage of federated learning and fuzzy inference mechanisms. We show that it is indeed possible to build a fuzzy inference model in a federated learning setting, resulting in a Federated Fuzzy Learning System (F2LS). We also show that this combination brings advantages to decision making that cannot be achieved with either mechanism in isolation.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116709479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494531
J. Kacprzyk, S. Zadrożny, H. Nurmi, A. Bozhenyuk
A new class of group decision making model under fuzzy preferences and a fuzzy majority is proposed which combines the traditional, widely employed and successful decision (making) by consensus, and the new idea, based on recent results from decision and, cognitive sciences, management science, psychology, etc. suggesting that decision by consensus may often lead to noninnovative enough decisions, and a different approach based on individuals (agents) who are not consensory but express different, maybe dissensory opinions, with a higher innovation potential, may be better. Here, in the new model, after the first phase of traditional decision by consensus, we use the concepts of Kacprzyk and Zadrożny's [28] consensory and dissensory agents, and then use primarily testimonies of dissensory agents which can imply innovative options to be chosen by using Kacprzyk [17], [18], and Kacprzyk, Zadrożny, Fedrizzi and Nurmi [29] group decision solution concepts, notably various fuzzy cores (i.e. fuzzy sets of options preferred over most other options).
{"title":"Towards innovation focused fuzzy decision making by consensus","authors":"J. Kacprzyk, S. Zadrożny, H. Nurmi, A. Bozhenyuk","doi":"10.1109/FUZZ45933.2021.9494531","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494531","url":null,"abstract":"A new class of group decision making model under fuzzy preferences and a fuzzy majority is proposed which combines the traditional, widely employed and successful decision (making) by consensus, and the new idea, based on recent results from decision and, cognitive sciences, management science, psychology, etc. suggesting that decision by consensus may often lead to noninnovative enough decisions, and a different approach based on individuals (agents) who are not consensory but express different, maybe dissensory opinions, with a higher innovation potential, may be better. Here, in the new model, after the first phase of traditional decision by consensus, we use the concepts of Kacprzyk and Zadrożny's [28] consensory and dissensory agents, and then use primarily testimonies of dissensory agents which can imply innovative options to be chosen by using Kacprzyk [17], [18], and Kacprzyk, Zadrożny, Fedrizzi and Nurmi [29] group decision solution concepts, notably various fuzzy cores (i.e. fuzzy sets of options preferred over most other options).","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126075131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494587
Yurii Bloshko, Z. Suraj, Oksana Olar
The paper presents a comparison of classical (ZtN, GtN, ZsN) and optimized (ZtN, ZtN, LsN) triples of functions for solving the hierarchical structure of the problem of passenger transport logistics. It covers the description of the subject area and its hierarchical scheme, the creation of production rules based on the knowledge of the experts, the creation of a weighted fuzzy Petri nets model with different triples, and the following analysis of the results. In order to illustrate the obtained results, a simulation was used in special software - PNeS®. The results of the experiment showed the development of the hierarchy and the following observation on calculations: the optimized triple of functions achieves higher output values, but at the same time, it can lead to an alternative decision.
{"title":"Towards Optimization of Triangular Norms in Weighted Fuzzy Petri Nets for Hierarchical Applications in Subject Area of Passenger Transport Logistics","authors":"Yurii Bloshko, Z. Suraj, Oksana Olar","doi":"10.1109/FUZZ45933.2021.9494587","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494587","url":null,"abstract":"The paper presents a comparison of classical (ZtN, GtN, ZsN) and optimized (ZtN, ZtN, LsN) triples of functions for solving the hierarchical structure of the problem of passenger transport logistics. It covers the description of the subject area and its hierarchical scheme, the creation of production rules based on the knowledge of the experts, the creation of a weighted fuzzy Petri nets model with different triples, and the following analysis of the results. In order to illustrate the obtained results, a simulation was used in special software - PNeS®. The results of the experiment showed the development of the hierarchy and the following observation on calculations: the optimized triple of functions achieves higher output values, but at the same time, it can lead to an alternative decision.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117077662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494543
Hyago R. M. Silva, R. Cardim, M. Teixeira, E. Assunção, Igor T. M. Ramos
Switched robust controllers are presented and applied to an aeropendulum system made at the Control Research Laboratory from DEE-FEIS-UNESP in Brazil. The system has nonlinear dynamics and it is approximated by Takagi-Sugeno fuzzy systems using linear local models for different operation points. Integrators are used in implementation to eliminate the steady-state error. The method consists of including a new state space variable in the system in order to obtain zero steady-state error. The switched control eliminates the need of finding the membership functions which are used to combine the local linear models of the Takagi-Sugeno fuzzy system. It is based on the minimization of the time derivative of the Lyapunov function and it is designed by means of linear matrix inequalities. Actuator saturation is also considered for the design of the controllers. At the end, implementations of the aforementioned controllers in the aeropendulum systems are presented considering the specifications of the decay rate and norm constraints of the controller gains.
{"title":"Switched Control and Tracking Application in Aeropendulum System using Fuzzy Models","authors":"Hyago R. M. Silva, R. Cardim, M. Teixeira, E. Assunção, Igor T. M. Ramos","doi":"10.1109/FUZZ45933.2021.9494543","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494543","url":null,"abstract":"Switched robust controllers are presented and applied to an aeropendulum system made at the Control Research Laboratory from DEE-FEIS-UNESP in Brazil. The system has nonlinear dynamics and it is approximated by Takagi-Sugeno fuzzy systems using linear local models for different operation points. Integrators are used in implementation to eliminate the steady-state error. The method consists of including a new state space variable in the system in order to obtain zero steady-state error. The switched control eliminates the need of finding the membership functions which are used to combine the local linear models of the Takagi-Sugeno fuzzy system. It is based on the minimization of the time derivative of the Lyapunov function and it is designed by means of linear matrix inequalities. Actuator saturation is also considered for the design of the controllers. At the end, implementations of the aforementioned controllers in the aeropendulum systems are presented considering the specifications of the decay rate and norm constraints of the controller gains.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129517632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494548
M. C. Rey-Merchán, Antonio López Arquillos, J. M. Soto-Hidalgo
With the increasing complexity of problems in the construction sector, fall from height is one of the most worrying in this sector. An appropriate use of a harness can be the difference between an incident or a critical accident. Traditionally, safety training, safety communication and onsite inspections are the habitual tools to manage the adequate use of harness. Despite on the availability of some technological solutions to monitor workers safety, their use are not frequent because some construction conditions. For this reason, the integration of technology and security expert knowledge in this task are a key issue. Different technological solutions, mainly based on computer vision approaches, have been proposed in this context. Nevertheless, these solutions lack ubiquitous computing, real time decisions capacity and expert knowledge management being crucial in this sector. In this context, Internet of Things (IoT) and Fuzzy Logic Systems (FLS) can provide several advantages: acquired data from sensors and real time decisions based on FLS. In this paper, the definition and use of an IoT infrastructure integrated with JFML, an open source library to FLS according to the IEEE std 1855, to support experts' decision making in fall from height are presented.
{"title":"A Fall from Height prevention proposal for construction sites based on Fuzzy Markup Language, JFML and IoT solutions","authors":"M. C. Rey-Merchán, Antonio López Arquillos, J. M. Soto-Hidalgo","doi":"10.1109/FUZZ45933.2021.9494548","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494548","url":null,"abstract":"With the increasing complexity of problems in the construction sector, fall from height is one of the most worrying in this sector. An appropriate use of a harness can be the difference between an incident or a critical accident. Traditionally, safety training, safety communication and onsite inspections are the habitual tools to manage the adequate use of harness. Despite on the availability of some technological solutions to monitor workers safety, their use are not frequent because some construction conditions. For this reason, the integration of technology and security expert knowledge in this task are a key issue. Different technological solutions, mainly based on computer vision approaches, have been proposed in this context. Nevertheless, these solutions lack ubiquitous computing, real time decisions capacity and expert knowledge management being crucial in this sector. In this context, Internet of Things (IoT) and Fuzzy Logic Systems (FLS) can provide several advantages: acquired data from sensors and real time decisions based on FLS. In this paper, the definition and use of an IoT infrastructure integrated with JFML, an open source library to FLS according to the IEEE std 1855, to support experts' decision making in fall from height are presented.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123995706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494389
Nhuan D. To, M. Reformat, R. Yager
The increased popularity of Linked Open Data (LOD) and advances in Natural Language Processing techniques have led to the development of Question Answering Systems (QASs) that utilize Knowledge Graphs as data sources. QASs perform well on simple questions providing precise and concise answers. Yet, most of them cannot process answers that contain a large volume of numerical values and are not able to provide users with answers in a human-friendly format. In this paper, we propose a user-defined method for constructing linguistic summarization of multi-feature data. It selects suitable summarizers and quantifiers and works with linguistic constraints imposed on the data. The method relies on definitions of linguistic terms constructed by users using an easy and simple graphical interface. Additionally, we introduce a Context-based User-defined Weighted Averaging (CUWA) operator. It allows determining an average value of data that satisfies multiple constraints that are account for the context defined by the user. We include several illustrative examples.
{"title":"Question-Answering System with Linguistic Summarization","authors":"Nhuan D. To, M. Reformat, R. Yager","doi":"10.1109/FUZZ45933.2021.9494389","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494389","url":null,"abstract":"The increased popularity of Linked Open Data (LOD) and advances in Natural Language Processing techniques have led to the development of Question Answering Systems (QASs) that utilize Knowledge Graphs as data sources. QASs perform well on simple questions providing precise and concise answers. Yet, most of them cannot process answers that contain a large volume of numerical values and are not able to provide users with answers in a human-friendly format. In this paper, we propose a user-defined method for constructing linguistic summarization of multi-feature data. It selects suitable summarizers and quantifiers and works with linguistic constraints imposed on the data. The method relies on definitions of linguistic terms constructed by users using an easy and simple graphical interface. Additionally, we introduce a Context-based User-defined Weighted Averaging (CUWA) operator. It allows determining an average value of data that satisfies multiple constraints that are account for the context defined by the user. We include several illustrative examples.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127950311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494346
E. H. Almansor, F. Hussain
Detecting breakdown is a common phenomenon in the conversational system, which is referred to when the system fails to provide appropriate responses to the user. Existing studies are detect breakdown using different features such as word similarity, topic transition, and clustering. In this paper, we focus on the different important feature, which is human thinking and reasoning. We use this feature to model chatbot quality of services (CQoS) based on detecting the breakdown. Thus we introduce the fuzzy prediction rule-based framework to measure chatbot quality of service by detecting the breakdown utterance considering end-user and chatbot points of view. Inputs utilized in the proposed fuzzy logic-based model are multiple useful features extracted from utterances. The outputs are the degrees of relevance for each utterance to the quality of services. Several fuzzy rules are designed, and the defuzzification method is used in order to achieve desired CQoS results. Based on the outputs from the fuzzy model, the handover mechanism will activate. We evaluate the proposed formwork with other state-of-the-art models.
{"title":"Fuzzy Prediction Model to Measure Chatbot Quality of Service","authors":"E. H. Almansor, F. Hussain","doi":"10.1109/FUZZ45933.2021.9494346","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494346","url":null,"abstract":"Detecting breakdown is a common phenomenon in the conversational system, which is referred to when the system fails to provide appropriate responses to the user. Existing studies are detect breakdown using different features such as word similarity, topic transition, and clustering. In this paper, we focus on the different important feature, which is human thinking and reasoning. We use this feature to model chatbot quality of services (CQoS) based on detecting the breakdown. Thus we introduce the fuzzy prediction rule-based framework to measure chatbot quality of service by detecting the breakdown utterance considering end-user and chatbot points of view. Inputs utilized in the proposed fuzzy logic-based model are multiple useful features extracted from utterances. The outputs are the degrees of relevance for each utterance to the quality of services. Several fuzzy rules are designed, and the defuzzification method is used in order to achieve desired CQoS results. Based on the outputs from the fuzzy model, the handover mechanism will activate. We evaluate the proposed formwork with other state-of-the-art models.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"102 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494565
Barbara Pekala, P. Drygas, M. Knap, Dorota Gil, Bogdan Kwiatkowski
In this paper, we concentrate on study interval-valued fuzzy relations in decision problems based on preference relations, and a preference structure making up of the strict preference relation, indifference relation, and incomparability relation which may be defined with the use of interval-valued aggregation and interval-valued fuzzy negation function. We analyze the influence of some new types of fusion functions on the effectiveness of the decision process. The studies concern different aggregation classes due to the type of monotonicity/order used.
{"title":"Preference and weak interval-valued operator in decision making problem","authors":"Barbara Pekala, P. Drygas, M. Knap, Dorota Gil, Bogdan Kwiatkowski","doi":"10.1109/FUZZ45933.2021.9494565","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494565","url":null,"abstract":"In this paper, we concentrate on study interval-valued fuzzy relations in decision problems based on preference relations, and a preference structure making up of the strict preference relation, indifference relation, and incomparability relation which may be defined with the use of interval-valued aggregation and interval-valued fuzzy negation function. We analyze the influence of some new types of fusion functions on the effectiveness of the decision process. The studies concern different aggregation classes due to the type of monotonicity/order used.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130220672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494498
Barbara Pekala, Dawid Kosior, Krzysztof Dyczkowski, Jaroslaw Szkola
The problem of measuring the degree of entropy based on precedence indicator and similarity measures under conditions of uncertainty or imprecision was studied. So we call back to the notion of precedence and similarity measures of interval-valued fuzzy sets (IVFSs) and we construct an entropy measure with uncertainty by applying for IVFSs of different orders. In addition, we discuss the impact of entropy measures reflecting the uncertainty in the decision-making problem that employed these new measures in the problem of missing values.
{"title":"Application of entropy measures with uncertainty in classification methods with missing data problem","authors":"Barbara Pekala, Dawid Kosior, Krzysztof Dyczkowski, Jaroslaw Szkola","doi":"10.1109/FUZZ45933.2021.9494498","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494498","url":null,"abstract":"The problem of measuring the degree of entropy based on precedence indicator and similarity measures under conditions of uncertainty or imprecision was studied. So we call back to the notion of precedence and similarity measures of interval-valued fuzzy sets (IVFSs) and we construct an entropy measure with uncertainty by applying for IVFSs of different orders. In addition, we discuss the impact of entropy measures reflecting the uncertainty in the decision-making problem that employed these new measures in the problem of missing values.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130437937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494488
J. Chamorro-Martínez, Míriam Mengíbar-Rodríguez, P. Martínez-Jiménez
Color is one of the most used features for image segmentation. However, two uncertainty problems arise in this scope: the color feature is imprecise by nature, and the boundaries between regions in real images are usually blurred. In this paper, we propose a fuzzy color-based image segmentation, where not only the obtained regions are fuzzy, but also the color feature used in this process. This way, our approach takes into account the blurriness of boundaries between regions, as well as the uncertainty associated to the color feature. On the one hand, colors are modeled through fuzzy sets related to the linguistic terms used by humans, providing membership degrees associated to them. On the other hand, we propose a path-based fuzzy segmentation technique, where the relationship between two pixels is quantified by means of fuzzy connectivity. This way, given a seed point, the connectivity between this point and the rest of pixels in the image can be used to obtain the corresponding fuzzy region.
{"title":"A Path-based Fuzzy Approach to Color Image Segmentation","authors":"J. Chamorro-Martínez, Míriam Mengíbar-Rodríguez, P. Martínez-Jiménez","doi":"10.1109/FUZZ45933.2021.9494488","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494488","url":null,"abstract":"Color is one of the most used features for image segmentation. However, two uncertainty problems arise in this scope: the color feature is imprecise by nature, and the boundaries between regions in real images are usually blurred. In this paper, we propose a fuzzy color-based image segmentation, where not only the obtained regions are fuzzy, but also the color feature used in this process. This way, our approach takes into account the blurriness of boundaries between regions, as well as the uncertainty associated to the color feature. On the one hand, colors are modeled through fuzzy sets related to the linguistic terms used by humans, providing membership degrees associated to them. On the other hand, we propose a path-based fuzzy segmentation technique, where the relationship between two pixels is quantified by means of fuzzy connectivity. This way, given a seed point, the connectivity between this point and the rest of pixels in the image can be used to obtain the corresponding fuzzy region.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134512298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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