Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

Hyun-Jin Kim,Kyungyong Chung,Ji-Won Baek

doi:10.1109/access.2021.3080180

Hyun-Jin Kim, Kyungyong Chung + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3080180

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 20	License type: CC BY 4.0

Affiliation: Kyonggi University, Sungkyunkwan University

Abstract

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.

Highlights

With the development of digital devices, image sensors for photographing are basically built in cameras, but smartphones and tablet PCs
This study proposes associative knowledge graph using fuzzy clustering and min-max normalization in video contents
ASSOCIATIVE KNOWLEDGE GRAPH USING FUZZY CLUSTERING AND MIN-MAX NORMALIZATION IN VIDEO CONTENTS Image content data are used in diverse areas

Summary

INTRODUCTION

With the development of digital devices, image sensors for photographing are basically built in cameras, but smartphones and tablet PCs. ASSOCIATIVE KNOWLEDGE GRAPH USING FUZZY CLUSTERING AND MIN-MAX NORMALIZATION IN VIDEO CONTENTS Image content data are used in diverse areas They offer different themes, they have hidden significant relations. In order to understand relations between the video content data generated in real-time and to extract important rule information, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. The proposed method is capable of improving the accuracy of association knowledge extraction in consideration of ambiguous relations between video content data with different objects. C. FUZZY CLUSTERING FOR EXTRACTION OF ASSOCIATIVE KNOWLEDGE GRAPH The Document Term Matrix redesigned through Shift and Reversed Min-Max Normalization includes weight values assigned according to object significance in each video. It is possible to find the association rule of clusters 1 and 2

RESULT

Findings

CONCLUSION