Analysis and Detection of Content based Video Retrieval

Gornale, Shivanand S.; Babaleshwar, Ashvini K; Babaleshwar, K

doi:10.5815/ijigsp.2019.03.06

Cited by 11 publications

(3 citation statements)

References 62 publications

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“…There is a lot of research considering content-based indexing and retrieval where precision enhancement is the major concern as in [11][12][13][14], some of them considered enhancing speed and accuracy [15,16]. However, a very large number of state-of-the-art researches on content-based indexing systems in the applications of content-based video search engines, are still far from acquiring fast retrieval for video records from index database due to matching and classification procedures, which affected the interactive use of such systems [17].…”

Section: Related Workmentioning

confidence: 99%

Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine

Adly¹,

Hegazy²,

Elarif³

et al. 2022

IJC

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Many research studies in content-based video search engines are concerned with content-based video queries retrieval where a query by example is sent to retrieve a list of visually similar videos. However, minor research is concerned with indexing and searching public video streaming services such as YouTube, where there is a dilemma for misusing copyrighted video materials and detecting bootleg manipulated videos before being uploaded. In this paper, a novel and effective technique for a content-based video search engine with effective detection of bootleg videos is evaluated on a large-scale video index dataset of 1088 video records. A novel feature vector is introduced using video shots temporal and key-object/concept features applying combinational-based matching algorithms, using various similarity metrics for evaluation. The retrieval system was evaluated using more than 200 non-semantic-based video queries evaluating both normal and bootleg videos, with retrieval precision for normal videos of 97.9% and retrieval recall of 100% combined by the F1 measure to be 98.3%. Bootleg videos retrieval precision scored 99.2% and retrieval recall was of 96.7% combined by the F1 measure to be 97.9%. This allows making a conclusion that this technique can help in enhancing both traditional text-based search engines and commonly used bootleg detection techniques.

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Section: Related Workmentioning

confidence: 99%

Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine

Adly¹,

Hegazy²,

Elarif³

et al. 2022

IJC

View full text Add to dashboard Cite

show abstract

“…However, the annotator can give different explanations for similar images. Also, text captions will be language-dependent [9]. The common feature of video retrieval by query systems in the literature is that when the query incomes, the system initiates a search on the database's recorded data.…”

Section: Introductionmentioning

confidence: 99%

Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

et al. 2021

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The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices selfjoin subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

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“…Videos retrieval approaches are important and necessary for multimedia applications like video search engines, digital archives, visual-on-demand broadcasting, etc. [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Content based video retrieval using discrete cosine transform

Hamad

Farhan

Khudhur

2021

IJEECS

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A content based video retrieval (CBVR)framework is built in this paper. One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.

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Analysis and Detection of Content based Video Retrieval

Cited by 11 publications

References 62 publications

Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine

Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine

Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

Content based video retrieval using discrete cosine transform

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