RETRACTED ARTICLE: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction

Prathiba, T.; Kumari, R. Shantha Selva

doi:10.1007/s12652-020-02190-w

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…New types of targets constantly influence the detection system, and the ability to self-learn is beneficial to improve the accuracy and speed of small target detection systems.  UHD video content retrieval can be used for rapid query and knowledge discovery of massive video information [24]. In industrial applications, video retrieval can assist managers in tracing defects and fault locations.…”

Section: Future Research Prospectsmentioning

confidence: 99%

52.2: Research on Industrial Application and Standards of Ultra HD Video

Jia

Zhao

2023

Symp Digest of Tech Papers

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Ultra high definition(UHD) industrial applications play a huge role, which can improve production efficiency, enhance product quality, and reduce resource waste. In the future, the industrial application of UHD video will be more extensive, but UHD is a long way to go to achieve total popularity. Therefore, there is an urgent need to develop a unified and open standard to promote the industrialization of UHD video. In this paper, we summarize the key technical aspects of UHD video industrial applications from the requirements of the UHD video industry. The standardization needs of industrial application areas are analyzed. According to the common problems and constraints in UHD video application, the standard system of UHD video industrial application is proposed, which contains five aspects: basic commonality, testing and certification, business presentation, auxiliary management, and field application. Finally, the shortcomings of UHD video industrial applications are pointed out, and possible solutions are given.

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Section: Future Research Prospectsmentioning

confidence: 99%

52.2: Research on Industrial Application and Standards of Ultra HD Video

Jia

Zhao

2023

Symp Digest of Tech Papers

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“…Preprocessing mainly completes the audio signal conversion, resampling, filtering, and signal enhancement [33]. The feature module is the representation of audio files.…”

Section: Multimedia Retrievalmentioning

confidence: 99%

Construction of law and economics litigation service platform based on multimedia retrieval

Cai

2023

Applied Mathematics and Nonlinear Sciences

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Legal economics uses empirical research to analyze and predict the effects of various legal system arrangements to better explain whether the actual effect of the law is consistent with the expected effect of the law. Artificial Intelligence (AI) has become the core driving force of the new round of scientific and technological revolutions, and the trend of cross-border integration is becoming more and more obvious. Therefore, the intelligent construction of a legal litigation platform is the only way for social development. Litigation service management is a platform that uses big data technology to process and facilitate the information development of legal litigation services. In the face of the rapid changes in legal litigation services, an intelligent litigation service management platform is needed to serve the public. Given the above defects, this paper proposes a law and economics litigation service management platform based on multimedia retrieval, including a litigation service management system, a queuing system, and an autonomous operating system. The litigation service management system is connected with the queuing system and autonomous operating system, respectively. We provide a litigation service management platform to provide efficient operations with humanized functions to assist and improve the service quality of legal litigation services. At the same time, we combined the current application status of AI in grassroots courts and the actual work to analyze the difficulties and problems faced, draw lessons from the successful experience of some courts in China, and find out the direction and path for the application and promotion of AI in grassroots courts.

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“…With the development of multimedia information technology, people often need to retrieve videos from the Internet, and video retrieval technology has become a hot topic. Common types of video retrieval include text-based queries [ 47 ], audio-based queries [ 48 ], and video-based queries [ 49 ]. In this paper we focus on retrieving videos through video queries, that is, for a given video, finding similar videos in a database.…”

Section: Related Workmentioning

confidence: 99%

A Supervised Video Hashing Method Based on a Deep 3D Convolutional Neural Network for Large-Scale Video Retrieval

Chen

Lee

et al. 2021

Sensors

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Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset.

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RETRACTED ARTICLE: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction

Cited by 14 publications

References 28 publications

52.2: Research on Industrial Application and Standards of Ultra HD Video

52.2: Research on Industrial Application and Standards of Ultra HD Video

Construction of law and economics litigation service platform based on multimedia retrieval

A Supervised Video Hashing Method Based on a Deep 3D Convolutional Neural Network for Large-Scale Video Retrieval

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