Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents
Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9430567/ |
| _version_ | 1818432569135333376 |
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| author | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung |
| author_facet | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung |
| author_sort | Hyun-Jin Kim |
| collection | DOAJ |
| description | Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence. |
| first_indexed | 2024-12-14T16:07:17Z |
| format | Article |
| id | doaj.art-d5a23e12f29e424ba5748fc479435894 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:07:17Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d5a23e12f29e424ba5748fc4794358942022-12-21T22:55:04ZengIEEEIEEE Access2169-35362021-01-019748027481610.1109/ACCESS.2021.30801809430567Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video ContentsHyun-Jin Kim0https://orcid.org/0000-0002-6390-0765Ji-Won Baek1https://orcid.org/0000-0001-9332-2815Kyungyong Chung2https://orcid.org/0000-0002-6439-9992Department of Artificial Intelligence, Sungkyunkwan University, Suwon-si, South KoreaDepartment of Computer Science, Kyonggi University, Suwon-si, South KoreaDivision of AI Computer Science and Engineering, Kyonggi University, Suwon-si, South KoreaVideo content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.https://ieeexplore.ieee.org/document/9430567/Associative knowledgefuzzy clusteringmin-max normalizationfuzzy theoryknowledge graph |
| spellingShingle | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents IEEE Access Associative knowledge fuzzy clustering min-max normalization fuzzy theory knowledge graph |
| title | Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents |
| title_full | Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents |
| title_fullStr | Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents |
| title_full_unstemmed | Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents |
| title_short | Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents |
| title_sort | associative knowledge graph using fuzzy clustering and min max normalization in video contents |
| topic | Associative knowledge fuzzy clustering min-max normalization fuzzy theory knowledge graph |
| url | https://ieeexplore.ieee.org/document/9430567/ |
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