• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.27-34
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• 1998

In this paper we present a method for automatic motion vector and brightness based video indexing and retrieval. We extract a representational frame from each shot and compute some motion vector and brightness based features. For each R-frame we compute the optical flow field; motion vector features are then derived from this flow field, BMA(block matching algorithm) is used to find motion vectors and Brightness features are related to the cut detection of method brightness histogram. A video database provided contents based access to video. This is achieved by organizing or indexing video data based on some set of features. In this paper the index of features is based on a B+ search tree. It consists of internal and leaf nodes stores in a direct access a storage device. This paper defines the problem of video indexing based on video data models.

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.127-137
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• 2006

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic- based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the automatic indexing agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we propose the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.471-475
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• 2005

Recently, development of digital technology is occupying a large part of multimedia information like character, voice, image, video, etc. Research about video indexing and retrieval progresses especially in research relative to video. This paper proposes the novel notation in order to retrieve MPEG video in the international standards of moving picture encoding For realizing the retrieval-system, we detect DCT DC coefficient, and then we obtain shot to apply MVC(Mean Value Comparative) notation to image constructed DC coefficient. We choose the key frame for start-frame of a shot, and we have the codebook index generating it using feature of DC image and applying PCA(principal Component Analysis) to the key frame. Also, we realize the retrieval-system through similarity after indexing. We could reduce error detection due to distinguish shot from conventional shot detection algorithm. In the mean time, speed of indexing is faster by PCA due to perform it in the compressed domain, and it has an advantage which is to generate codebook due to use statistical features. Finally, we could realize efficient retrieval-system using MVC and PCA to shot detection and indexing which is important step of retrieval-system, and we using retrieval-system over the internet.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.101-104
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• 2001

This paper proposes a unique shot boundary detection algorithm for the video indexing and/or browsing. Conventional methods based on the frame differences and the histogram differences are improved. Instead of using absolute frame differences, block by block based relative frame differences are employed. Frame adaptive thresholding values are also employed for the better detection. for the cases that the frame differences are not enough to detect the shot boundary, histogram differences are selectively applied. Experimental results show that the proposed algorithm reduces both the “false positive” errors and the “false negative” errors especially for the videos of dynamic local and/or global motions

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.701-708
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• 2004

Existing contents-based video retrieval systems search by using a single method such as annotation-based or feature-based retrieval. Hence, it not only shows low search efficiency, but also requires many efforts to provide system administrator or annotator with a perfect automatic processing. Tn this paper, we propose an agent-based, and automatic and unified semantics-based video retrieval system, which support various semantics-retrieval of the massive video data by integrating the feature-based retrieval and the annotation-based retrieval. The indexing agent embodies the semantics about annotation of extracted key frames by analyzing a fundamental query of a user and by selecting a key-frame image that is ed by a query. Also, a key frame selected by user takes a query image of the feature-based retrieval and the indexing agent searches and displays the most similar key-frame images after comparing query images with key frames in the database by using the color-multiple-partition histogram techniques. Furthermore, it is shown that the performance of the proposed system can be significantly improved.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.79-87
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• 2002

The fast advances in digital video processing and multimedia processing technology over the last decade enabled various non-linear video browsing techniques. Based on the machine-understanding of the video content, non-linear video brows ing interfaces such as key-frame based content summarization have been introduced. The key-frame based user interfaces, such as storyboard or table of content, however, are still very hard for conventional TV users to use, and are very hard to implement without the service providers providing additional information for the construction of the key-frame based interfaces. In this paper, non-linear video browsing techniques, which not only overcome previously described drawbacks but also are easy-to-use, and real-time video indexing technology to support the proposed browsing techniques are proposed. The structure-based skipping and skimming help users easily find interesting scene and understand the content in a very short time, using real-time video indexing technology.

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.2
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• pp.90-103
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• 2006

There are many file systems in various operating systems. Those are usually designed for server environments, where the common cases are usually 'multiple active users', 'great many small files' And they assume a big main memory to be used as buffer cache. So the existing file systems are not suitable for resource hungry embedded systems that process multimedia data streams. In this study, we designed and implemented a new file system which efficiently stores and retrieves multimedia data steams. The proposed file system has a very simple disk layout, which guarantees a quick disk initialization and file system recovery. And we introduced a new indexing-scheme, called the time-based indexing scheme, with the file system. With the indexing scheme, the file system maintains the relation between time and the location for all the multimedia streams. The scheme is useful in searching and playing the compressed multimedia streams by locating exact frame position with given time, resulting in reduction of CPU processing and power consumption. The proposed file system and its APIs utilizing the time-based indexing schemes were implemented firstly on a Linux environment, though it is operating system independent. In the performance evaluation on a real DVR system, which measured the execution time of multi-threaded reading and writing, we found the proposed file system is maximum 38.7% faster than EXT2 file system.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1050-1060
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• 2011

With increasing need for indexing of persons in a large video database, automatic indexing has been attracting great interest which takes advantage of automatic tagging instead of the time-consuming and costly manual tagging. However, automatic indexing approach should provide a degree of recognition proximity because it cannot identify the persons with accuracy of 100%. In this paper, we propose an efficient storage method for storing posting lists efficiently and a novel ranking technique of ordering relevant videos for efficient retrieval. Through experiment evaluations we have shown that our storage method exhibits good performance in compressing the posting list. We have also shown that the proposed ranking method is effective for finding relevant videos.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.687-694
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• 2003

According to the rapid increase of multimedia data quantity recently, various means of video data search has been desired. In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.