A comprehensive study in novel content based video retrieval using vector quantization over a diversity of color spaces

Gaikwad, Rahul; Neve, Jitesh R.

doi:10.1109/icgtspicc.2016.7955266

Cited by 4 publications

(3 citation statements)

References 8 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

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“…Unlike CBIR, the Text-Based Image Retrieval (TBIR) method annotates the images with file name, image size, dimension, and format. Then, it stores them in a database [3]. The first disadvantage of the TBIR is creating metadata manually for an extensive database is impractical.…”

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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“…In the last two decades, video content analysis has attracted considerable attention, and relevant approaches can be divided into two categories. The first is objective content analysis, which aims at extracting “fact”-based information, including content extraction [7,8,9], audio-visual affective extraction [10,11,12,13,14], event detection [15,16], etc. The second is affective video analysis, where feelings or emotions within the clip are analyzed [17,18].…”

Section: Introductionmentioning

confidence: 99%

Two-Way Affective Modeling for Hidden Movie Highlights’ Extraction

Wang

Yan

Jiang

et al. 2018

Sensors

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Movie highlights are composed of video segments that induce a steady increase of the audience’s excitement. Automatic movie highlights’ extraction plays an important role in content analysis, ranking, indexing, and trailer production. To address this challenging problem, previous work suggested a direct mapping from low-level features to high-level perceptual categories. However, they only considered the highlight as intense scenes, like fighting, shooting, and explosions. Many hidden highlights are ignored because their low-level features’ values are too low. Driven by cognitive psychology analysis, combined top-down and bottom-up processing is utilized to derive the proposed two-way excitement model. Under the criteria of global sensitivity and local abnormality, middle-level features are extracted in excitement modeling to bridge the gap between the feature space and the high-level perceptual space. To validate the proposed approach, a group of well-known movies covering several typical types is employed. Quantitative assessment using the determined excitement levels has indicated that the proposed method produces promising results in movie highlights’ extraction, even if the response in the low-level audio-visual feature space is low.

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A comprehensive study in novel content based video retrieval using vector quantization over a diversity of color spaces

Cited by 4 publications

References 8 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

Two-Way Affective Modeling for Hidden Movie Highlights’ Extraction

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