• International Journal of Contents
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• v.5 no.4
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• pp.51-54
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• 2009

In huge video databases, the effective video content indexing method is required. While manual indexing is the most effective approach to this goal, it is slow and expensive. Thus automatic indexing is desirable and recently various indexing tools for video databases have been developed. For efficient video content indexing, the similarity measure is an important factor. This paper presents new similarity measures between frames and proposes a new algorithm to index video content using Kullback-Leibler distance defined between two histograms. Experimental results show that the proposed algorithm using Kullback-Leibler distance gives remarkable high accuracy ratios compared with several conventional algorithms to index video content.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.891-898
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• 2004

An effective video indexing is required to manipulate large video databases. Most algorithms for video indexing have been commonly used histograms, edges, or motion features. In this paper, we propose an efficient algorithm using the luminance projection for video retrieval. To effectively index the video sequences and to reduce the computational complexity, we use the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed video indexing and video retrieval algorithm yields the higher accuracy and performance than the conventional algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• ETRI Journal
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• v.31 no.1
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• pp.10-20
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• 2009

In this paper, a video broadcasting system between a home-server-type device and a mobile device is proposed. The home-server-type device can automatically extract semantic information from video contents, such as news, a soccer match, and a baseball game. The indexing results are utilized to convert the original video contents to a digested or arranged format. From the mobile device, a user can make recording requests to the home-server-type devices and can then watch and navigate recorded video contents in a digested form. The novelty of this study is the actual implementation of the proposed system by combining the actual IT environment that is available with indexing algorithms. The implementation of the system is demonstrated along with experimental results of the automatic video indexing algorithms. The overall performance of the developed system is compared with existing state-of-the-art personal video recording products.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1570-1578
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• 2000

Object motion is an important feature of content in video sequences. By now, various methods to exact feature about the object motion have been reported[1,2]. However they are not suitable to index video using the motion, since a lot of bits and complex indexing parameters are needed for the indexing [3,4] In this paper, we propose object motion map which could provide efficient indexing method for object motion. The proposed object motion map has both global and local motion information during an object is moving. Furthermore, it requires small bit of memory for the indexing. to evaluate performance of proposed indexing technique, experiments are performed with video database consisting of MPEG-1 video sequence in MPEG-7 test set.

• International Journal of Contents
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• v.12 no.1
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• pp.1-5
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• 2016

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.173-189
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• 2009

Recently, video data has attracted many interest. That is the reason why efficient indexing schemes are required to support the content-based retrieval of video data. But most indexing schemes are not suitable for indexing a high-dimensional data except Hybrid Spill-Tree. In this paper, we propose an efficient high-dimensional indexing scheme to support the content-based retrieval of video data. For this, we extend Hybrid Spill-Tree by using a newly designed clustering technique and by adopting a signature method. Finally, we show that proposed signature-based high dimensional indexing scheme achieves better retrieval performance than existing M-Tree and Hybrid Spill-Tree.

• Journal of Broadcast Engineering
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• v.9 no.2
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• pp.151-163
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• 2004

In this paper, we propose a content-based video indexing and retrieval system using MPEG-7 standard to retrieve and manage videos efficiently. The proposed system consists of video indexing module for a video DB and video retrieval module to allow various query methods on a web environment. Video indexing module stores metadata such as manually typed in keywords, automatically recognized character names, and MPEG-7 visual descriptors extracted by indexing module into a DB in a sever side. A user can access to retrieval module by a web and retrieve desired videos through various query methods like keywords, faces, example and sketch. For this retrieval system, we propose ATC(Adaptive Twin Comparison) as a cut detection method for efficient video indexing and QBME(Query By Modified Example) as an improved content-based query method for the convenience of users. Experimental results show that the proposed ATC method detects cuts well and the proposed QBME method provides the conveniences better than existing query methods such as QBE(Query By Example) and QBS(Query By Sketch).

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.16-20
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• 2001

An important aspect of video program indexing and retrieval is the ability to segment video program into meaningful segments, in other words, the ability of content-based video program segmentation. In this paper, a new approach using speech recognition technology has been proposed for content-based video program segmentation. This approach uses speech recognition technique to synchronize closed caption with speech signal. Experimental results demonstrate that the proposed scheme is very promising for content-based video program segmentation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.35-44
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• 2004

With increase in digitalmedia data, various video indexing and video sequence matching algorithms have been proposed to efficiently manage and utilize digital media. In this paper, we propose a robust video indexing algorithm to detect scene changes for video sequences with abrupt luminance variations and an efficient video sequence matching algorithm for video sequence query. To improve the accuracy and to reduce the computational complexity for video indexing with abrupt luminance variations, the proposed algorithm utilizes edge features as well as color features, which yields a remarkably better performance than conventional algorithms. In the proposed algorithm first we extract the candidate shot boundaries using color histograms and then determine using edge matching and luminance compensation if they are shot boundaries or luminance changes. If the scene contains trivial brighness variations, the edge matching and luminance compensation are performed only for shot boundaries. In experimental results, the proposed method gives remarkably a high performance and efficiency than the conventional methods with the similar computational complexity.