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Journal articles on the topic 'Content-Based Video Retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Hamad, Sumaya, Ahmeed Suliman Farhan, and Doaa Yaseen Khudhur. "Content based video retrieval using discrete cosine transform." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 839–45. https://doi.org/10.11591/ijeecs.v21.i2.pp839-845.

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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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2

Sheetal, Deepak Patil. "Content Based Image and Video Retrieval: A Compressive Review." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 243–47. https://doi.org/10.35940/ijeat.E2783.0610521.

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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d

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3

Patel, B. V. "Content Based Video Retrieval." International journal of Multimedia & Its Applications 4, no. 5 (2012): 77–98. http://dx.doi.org/10.5121/ijma.2012.4506.

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4

K. D, Wagh, and Dr Kharat M. U. "Content Based Video Retrieval." IJARCCE 5, no. 1 (2016): 53–58. http://dx.doi.org/10.17148/ijarcce.2016.5112.

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5

Waykar, Sanjay B., and C. R. Bharathi. "Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval." Journal of Intelligent Systems 26, no. 3 (2017): 585–99. http://dx.doi.org/10.1515/jisys-2016-0041.

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AbstractDue to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lectu

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6

Lin, Lin, and Mei-Ling Shyu. "Correlation-Based Ranking for Large-Scale Video Concept Retrieval." International Journal of Multimedia Data Engineering and Management 1, no. 4 (2010): 60–74. http://dx.doi.org/10.4018/jmdem.2010100105.

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Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transacti

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7

Patil, Sheetal Deepak. "Content Based Image and Video Retrieval A Compressive Review." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 243–47. http://dx.doi.org/10.35940/ijeat.e2783.0610521.

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Abstract:

Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d

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8

Hamad, Sumaya, Ahmeed Suliman Farhan, and Doaa Yaseen Khudhur. "Content based video retrieval using discrete cosine transform." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 839. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp839-845.

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Abstract:

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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9

Chavan, Smita, and Shubhangi Sapkal. "Color Content based Video Retrieval." International Journal of Computer Applications 84, no. 11 (2013): 15–18. http://dx.doi.org/10.5120/14619-2931.

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10

Patel, B. V. "Content Based Video Retrieval Systems." International Journal of UbiComp 3, no. 2 (2012): 13–30. http://dx.doi.org/10.5121/iju.2012.3202.

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11

., Madhav Gitte. "CONTENT BASED VIDEO RETRIEVAL SYSTEM." International Journal of Research in Engineering and Technology 03, no. 06 (2014): 430–35. http://dx.doi.org/10.15623/ijret.2014.0306079.

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12

Thinh, Bui Van, Tran Anh Tuan, Ngo Quoc Viet, and Pham The Bao. "Content based video retrieval system using principal object analysis." Tạp chí Khoa học 14, no. 9 (2019): 24. http://dx.doi.org/10.54607/hcmue.js.14.9.291(2017).

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Video retrieval is a searching problem on videos or clips based on the content of video clips which relates to the input image or video. Some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that,

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13

Hussain, Altaf, Mehtab Ahmad, Tariq Hussain, and Ijaz Ullah. "Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 2 (2022): 207–35. http://dx.doi.org/10.14201/adcaij.27430.

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The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database.

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14

V Khasne, Mukul, Kishor P Jadhav, and Rina S Patil. "Automatic Meaningful Video Content Retrieval Based on Conceptual Fuzzy Model." International Journal of Scientific Engineering and Research 4, no. 5 (2016): 27–30. https://doi.org/10.70729/ijser15805.

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15

CHEN, SHU-CHING, NA ZHAO, and MEI-LING SHYU. "MODELING SEMANTIC CONCEPTS AND USER PREFERENCES IN CONTENT-BASED VIDEO RETRIEVAL." International Journal of Semantic Computing 01, no. 03 (2007): 377–402. http://dx.doi.org/10.1142/s1793351x07000159.

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In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequ

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16

NGO, CHONG-WAH, TING-CHUEN PONG, and HONG-JIANG ZHANG. "RECENT ADVANCES IN CONTENT-BASED VIDEO ANALYSIS." International Journal of Image and Graphics 01, no. 03 (2001): 445–68. http://dx.doi.org/10.1142/s0219467801000268.

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In this paper, we present major issues in video parsing, abstraction, retrieval and semantic analysis. We discuss the success, the difficulties and the expectations in these areas. In addition, we identify important opened problems that can lead to more sophisticated ways of video content analysis. For video parsing, we discuss topics in video partitioning, motion characterization and object segmentation. The success in video parsing, in general, will have a great impact on video representation and retrieval. We present three levels of abstracting video content by scene, keyframe and key objec

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17

Rajendran, Priya, and T. N. Shanmugam. "A content-based video retrieval system: video retrieval with extensive features." International Journal of Multimedia Intelligence and Security 2, no. 2 (2011): 146. http://dx.doi.org/10.1504/ijmis.2011.041363.

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18

Jacob, Jaimon, Sudeep Ilayidom, and V. P. Devassia. "Content Based Video Retrieval System Using Video Indexing." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 478–782. http://dx.doi.org/10.26438/ijcse/v7i4.478782.

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19

Bae, Jong-Sik, Hae-Sool Yang, and Hyung-Jin Choi. "Content-based News Video Retrieval System." Journal of the Korea Contents Association 11, no. 2 (2011): 54–60. http://dx.doi.org/10.5392/jkca.2011.11.2.054.

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20

HPatel, Dipika. "Content based Video Retrieval: A Survey." International Journal of Computer Applications 109, no. 13 (2015): 1–5. http://dx.doi.org/10.5120/19245-0596.

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21

Roth, Volker. "Content-based retrieval from digital video." Image and Vision Computing 17, no. 7 (1999): 531–40. http://dx.doi.org/10.1016/s0262-8856(98)00144-9.

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22

Vasconcelos, Nuno. "Content-Based Image and Video Retrieval." Signal Processing 85, no. 2 (2005): 231–32. http://dx.doi.org/10.1016/j.sigpro.2004.10.007.

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23

Smoliar, S. W., and HongJiang Zhang. "Content based video indexing and retrieval." IEEE Multimedia 1, no. 2 (1994): 62–72. http://dx.doi.org/10.1109/93.311653.

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24

Gu, Lingchen, Ju Liu, and Aixi Qu. "Performance Evaluation and Scheme Selection of Shot Boundary Detection and Keyframe Extraction in Content-Based Video Retrieval." International Journal of Digital Crime and Forensics 9, no. 4 (2017): 15–29. http://dx.doi.org/10.4018/ijdcf.2017100102.

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The advancement of multimedia technology has contributed to a large number of videos, so it is important to know how to retrieve information from video, especially for crime prevention and forensics. For the convenience of retrieving video data, content-based video retrieval (CBVR) has got great publicity. Aiming at improving the retrieval performance, we focus on the two key technologies: shot boundary detection and keyframe extraction. After being compared with pixel analysis and chi-square histogram, histogram-based method is chosen in this paper. Then we combine it with adaptive threshold

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25

Song, Yaguang, Junyu Gao, Xiaoshan Yang, and Changsheng Xu. "Learning Hierarchical Video Graph Networks for One-Stop Video Delivery." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (2022): 1–23. http://dx.doi.org/10.1145/3466886.

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The explosive growth of video data has brought great challenges to video retrieval, which aims to find out related videos from a video collection. Most users are usually not interested in all the content of retrieved videos but have a more fine-grained need. In the meantime, most existing methods can only return a ranked list of retrieved videos lacking a proper way to present the video content. In this paper, we introduce a distinctively new task, namely One-Stop Video Delivery (OSVD) aiming to realize a comprehensive retrieval system with the following merits: it not only retrieves the relev

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26

Nasreen, Azra, and Shobha G. "Parallelizing Multi-featured Content Based Search and Retrieval of Videos through High Performance Computing." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 214. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp214-219.

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<p>Video Retrieval is an important technology that helps to design video search engines and allow users to browse and retrieve videos of interest from huge databases. Though, there are many existing techniques to search and retrieve videos based on spatial and temporal features but are unable to perform well resulting in high ranking of irrelevant videos leading to poor user satisfaction. In this paper an efficient multi-featured method for matching and extraction is proposed in parallel paradigm to retrieve videos accurately and quickly from the collection. Proposed system is tested on

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27

Ablaoui, Linda, Wilson Estecio Marcilio-Jr, Lai Xing Ng, Christophe Jouffrais, and Christophe Hurter. "Interactive Content Retrieval in Egocentric Videos Based on Vague Semantic Queries." Multimodal Technologies and Interaction 9, no. 7 (2025): 66. https://doi.org/10.3390/mti9070066.

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Retrieving specific, often instantaneous, content from hours-long egocentric video footage based on hazily remembered details is challenging. Vision–language models (VLMs) have been employed to enable zero-shot textual-based content retrieval from videos. But, they fall short if the textual query contains ambiguous terms or users fail to specify their queries enough, leading to vague semantic queries. Such queries can refer to several different video moments, not all of which can be relevant, making pinpointing content harder. We investigate the requirements for an egocentric video content ret

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28

Anita, Mukund Pujar, and Arvind Domal Pratibha. "A Real Time Video Streaming Web Portal." Journal of Android and IOS Applications and Testing 4, no. 3 (2019): 16–20. https://doi.org/10.5281/zenodo.3568932.

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<em>Content-based video extracting is very essential now-a-days. The existing data mining algorithms are not directly applied to videos. This proposed work uses different data mining algorithms for indexing, clustering, searching and retrieving content-based videos. A system will be developed in which only admin can upload videos on cloud server. Videos are sorted based on category and videos are automatically uploaded on Cloud Server on the schedule provided by the admin. Users can watch videos online, they can download videos based on video summary and users can rate videos that will be anal

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29

Chen, Li Wei, and Yong Li Gao. "Research and Realization Based on Content Video Retrieval." Applied Mechanics and Materials 55-57 (May 2011): 2163–68. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.2163.

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Along with the multimedia technological development and the large-scale database widespread application, the content video retrieval technology obtains the rapid development, at the same time, the algorithms were also proposed. This article has carried on the induction summary to these new theory technologies based on the content video retrieval demonstration system. This article first introduced based on the content video retrieval essential technology including regards the lens the boundary examination and the division, the essential frame selection the characteristic withdraws, the similar

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30

Kanagaraj, Kaavya, Shilpa Abhang, Julakanti Sampath Kumar, Gnanamurthy R.K., and Balaji V. "AI-BASED VIDEO SUMMARIZATION FOR EFFICIENT CONTENT RETRIEVAL." ICTACT Journal on Image and Video Processing 14, no. 2 (2023): 3137–42. http://dx.doi.org/10.21917/ijivp.2023.0446.

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The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomousl

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31

M.Kamde, P., Sankirti Shiravale, and S. P. Algur. "Entropy Supported Video Indexing for Content based Video Retrieval." International Journal of Computer Applications 62, no. 17 (2013): 1–6. http://dx.doi.org/10.5120/10169-9974.

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32

Mohamadzadeh, Sajad, and Hassan Farsi. "CONTENT BASED VIDEO RETRIEVAL BASED ON HDWT AND SPARSE REPRESENTATION." Image Analysis & Stereology 35, no. 2 (2016): 67. http://dx.doi.org/10.5566/ias.1346.

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Video retrieval has recently attracted a lot of research attention due to the exponential growth of video datasets and the internet. Content based video retrieval (CBVR) systems are very useful for a wide range of applications with several type of data such as visual, audio and metadata. In this paper, we are only using the visual information from the video. Shot boundary detection, key frame extraction, and video retrieval are three important parts of CBVR systems. In this paper, we have modified and proposed new methods for the three important parts of our CBVR system. Meanwhile, the local a

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33

Geetha, P., and Vasumathi Narayanan. "A Survey of Content-Based Video Retrieval." Journal of Computer Science 4, no. 6 (2008): 474–86. http://dx.doi.org/10.3844/jcssp.2008.474.486.

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34

Patil, Dipali, and M. A. Potey. "Survey of Content Based Lecture Video Retrieval." International Journal of Computer Trends and Technology 19, no. 1 (2015): 5–8. http://dx.doi.org/10.14445/22312803/ijctt-v19p102.

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35

DUC NGO, Thanh, Duy DINH LE, and Shin'ichi SATOH. "Scalable approaches for content based video retrieval." Progress in Informatics, no. 11 (March 2014): 31. http://dx.doi.org/10.2201/niipi.2014.11.5.

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36

., Deshmukh Bhagyashri D. "REVIEW ON CONTENT BASED VIDEO LECTURE RETRIEVAL." International Journal of Research in Engineering and Technology 03, no. 11 (2014): 306–12. http://dx.doi.org/10.15623/ijret.2014.0311050.

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37

Doulamis, Anastasios D., Nikolaos D. Doulamis, and Stefanos D. Kollias. "A fuzzy video content representation for video summarization and content-based retrieval." Signal Processing 80, no. 6 (2000): 1049–67. http://dx.doi.org/10.1016/s0165-1684(00)00019-0.

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38

MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user appli

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39

Yan, Ming Liang. "Research on the Content-Based Video Indexing for Smart Grid." Advanced Materials Research 945-949 (June 2014): 3391–95. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3391.

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Data has become the fundamental resource by the emerging new services such as cloud computing, internet of things and social network. In the electric power applications, the video data mining plays an important role in the intelligent data analysis. With growth of video data in such an amazing speed, the information retrieval is becoming more and more important. This paper focuses on the analysis of the content-based video retrieval and proposes the design of a uniformed search engine system. The system is oriented to the retrieval of both the unstructured video contents and structured tags, w

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40

MARTIANO, DH, K. CHARRIERE, M. LAMARD, and B. COCHENER. "Indexing of cataract surgery video by content based video retrieval." Acta Ophthalmologica 92 (August 20, 2014): 0. http://dx.doi.org/10.1111/j.1755-3768.2014.s096.x.

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41

LIN, TONG, HONG-JIANG ZHANG, and QING-YUN SHI. "VIDEO CONTENT REPRESENTATION FOR SHOT RETRIEVAL AND SCENE EXTRACTION." International Journal of Image and Graphics 01, no. 03 (2001): 507–26. http://dx.doi.org/10.1142/s0219467801000293.

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In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similar

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42

Mittal, Ankush, and Sumit Gupta. "Automatic content-based retrieval and semantic classification of video content." International Journal on Digital Libraries 6, no. 1 (2006): 30–38. http://dx.doi.org/10.1007/s00799-005-0119-y.

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43

Maitree, Nagariya, U. K. Jaliya, and M. S. Holia. "A Survey on Content-Based Video Retrieval Techniques." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 878–83. http://dx.doi.org/10.26438/ijcse/v7i2.878883.

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Gornale, Shivanand S., Ashvini K. Babaleshwar, and Pravin L. Yannawar. "Content Based Video Retrieval for Indian Traffic Signages." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 14–20. http://dx.doi.org/10.26438/ijcse/v7i5.1420.

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HPatel, Dipika. "Content based Video Retrieval using Enhance Feature Extraction." International Journal of Computer Applications 119, no. 19 (2015): 4–8. http://dx.doi.org/10.5120/21173-4052.

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Kim, Nac-Woo, Byung-Tak Lee, Jai-Sang Koh, and Ho-Young Song. "A new approach for content-based video retrieval." International Journal of Contents 4, no. 2 (2008): 24–28. http://dx.doi.org/10.5392/ijoc.2008.4.2.024.

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S Gornale, Shivanand, Ashvini K Babaleshwar, and K. Babaleshwar. "Analysis and Detection of Content based Video Retrieval." International Journal of Image, Graphics and Signal Processing 11, no. 3 (2019): 43–57. http://dx.doi.org/10.5815/ijigsp.2019.03.06.

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Hyun Sung Chang, Sanghoon Sull, and Sang Uk Lee. "Efficient video indexing scheme for content-based retrieval." IEEE Transactions on Circuits and Systems for Video Technology 9, no. 8 (1999): 1269–79. http://dx.doi.org/10.1109/76.809161.

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49

Spolaôr, Newton, Huei Diana Lee, Weber Shoity Resende Takaki, Leandro Augusto Ensina, Claudio Saddy Rodrigues Coy, and Feng Chung Wu. "A systematic review on content-based video retrieval." Engineering Applications of Artificial Intelligence 90 (April 2020): 103557. http://dx.doi.org/10.1016/j.engappai.2020.103557.

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Mu, Xiangming. "Semantic visual features in content-based video retrieval." Proceedings of the American Society for Information Science and Technology 43, no. 1 (2007): 1–14. http://dx.doi.org/10.1002/meet.1450430153.

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