• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.319-321
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• 2003

웹 상에서 제공되는 영화 서비스는 사용자에게 시간과 공간을 초월하여 서비스를 받을 수 있다는 매력을 가지고 있다. 이러한 영화 서비스는 대부분 동영상 전체 내용에 대해 사용자 연령별로 등급을 나누어 서비스되거나 무차별 서비스가 되고 있다. 본 논문에서는 비디오 인덱싱 기술과 필터링 기법을 접목하여 하나의 동영상을 각 장면의 내용에 따라 사용자 연령별 차별화 된 서비스를 함으로써 영화 동영상에 대한 활용도를 높일 수 있는 방법을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3520-3537
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• 2014

Video copy detection methods have emerged in recent years for a variety of applications. However, the lack of efficiency in the usual retrieval systems restricts their use. In this paper, we propose a parallel implementation strategy for content based video copy detection (CBCD) by using a multi-core processor. This strategy can support video copy detection effectively, and the processing time tends to decrease linearly as the number of processors increases. Experiments have shown that our approach is successful in speeding up computation and as well as in keeping the performance.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.89-98
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• 2007

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1527-1530
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• 2003

Video is an important and challenge media and requires sophisticated indexing schemes for efficient retrieval from visual databases. Scene change detection is the first step for automatic indexing of video data. Recently, several scene change detection algorithms in the pixel and compressed domains have been reported in the literature. However, using pixel methods are computationally complex and are not very robust in detecting scene change detection. In this paper, we propose robust abrupt scene change detection using macroblock type and DC coefficient. Experimental results show that the proposed algorithm is robust for detection of most abrupt scene changes in the compressed domain.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• Journal of Information Management
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• v.37 no.4
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• pp.93-104
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• 2006

With the advancement of internet and video compression technology, there has been an increasing demand for video, and producted a large quantity contents of UCC. Therefore, Semantic-sensitive retrieval and construction for digital video library is more in demand than ever. However, it is extremely difficult to categorize and label scenes in any video automatically for searching wanted scene. This study proposes a method to extract certain scenes and analyze the video content, and shows the experimental results after categorizing 5 sports news(soccer, baseball, golf, basketball, and volleyball).

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.47-56
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• 2005

A moving object has a various features that its spatial location, shape, and size are changed as time goes. In addition, the moving object has both temporal feature and spatial feature. It is one of the highly interested feature information in video data. In this paper, we propose an efficient content-based multimedia information retrieval system, so tailed ECoMOT which enables user to retrieve video data by using a trajectory information of moving objects in video data. The ECoMOT includes several novel techniques to achieve content-based retrieval using moving objects' trajectories : (1) Muitiple trajectory modeling technique to model the multiple trajectories composed of several moving objects; (2) Multiple similar trajectory retrieval technique to retrieve more similar trajectories by measuring similarity between a given two trajectories composed of several moving objects; (3) Superimposed signature-based trajectory indexing technique to effectively search corresponding trajectories from a large trajectory databases; (4) convenient trajectory extraction, query generation, and retrieval interface based on graphic user interface

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.10-19
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• 2003

Caption information in news videos can be useful for video indexing and retrieval since it usually suggests or implies the contents of the video very well. In this paper, a new algorithm for extracting and recognizing characters from news video is proposed, without a priori knowledge such as font type, color, size of character. In the process of text region extraction, in order to improve the recognition rate for videos with complex background at low resolution, continuous frames with identical text regions are automatically detected to compose an average frame. The image of the averaged frame is projected to horizontal and vertical direction, and we apply region filling to remove backgrounds to produce the character. Then, K-means color clustering is applied to remove remaining backgrounds to produce the final text image. In the process of character recognition, simple features such as white run and zero-one transition from the center, are extracted from unknown characters. These feature are compared with the pre-composed character feature set to recognize the characters. Experimental results tested on various news videos show that the proposed method is superior in terms of caption extraction ability and character recognition rate.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.673-681
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• 2004

As the use of digital videos is getting popular, there is an increasing demand for efficient browsing and retrieval of video. To support such operations, effective video indexing should be incorporated. One of the most fundamental steps in video indexing is to parse video stream into shots and scenes. Generally, it takes long time to parse a video due to the huge amount of computation in a traditional single computing environment. Previous studies had widely used Round Robin scheduling which basically allocates tasks to each slave for a time interval of one quantum. This scheduling is difficult to adapt in a heterogeneous environment. In this paper, we propose two different parallel parsing algorithms which are Size-Adaptive Round Robin and Dynamic Size-Adaptive Round Robin for the heterogeneous distributed computing environments. In order to show their performance, we perform several experiments and show some of the results.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1410-1417
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• 1999

In this paper, we propose the method of extracting the caption regions from news video and the method of recognizing the captions that can be used mainly for content-based indexing and retrieving the MPEG-2 compressed news for NOD(News On Demand). The proposed method can reduce the searching time on detecting caption frames with minimum MPEG-2 decoding, and effectively eliminate the noise in caption regions by deliberately devised preprocessing. Because the kind of fonts that are used for captions is not various in the news video, an enhanced template matching method is used for recognizing characters. We could obtain good recognition result in the experiment of sports news video by the proposed methods.