• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.661-683
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• 2001

There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage video databases efficiently. The development of high speed data network and digital techniques has emerged new multimedia applications such as internet broadcasting, Video On Demand(VOD) combined with video data processing and computer. Video database should be construct for searching fast, efficient video be extract the accurate feature information of video with more massive and more complex characteristics. Video database are essential differences between video databases and traditional databases. These differences lead to interesting new issues in searching of video, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of video. In this paper, We propose the construction and generation method of the video database based on contents which is able to accumulate the meaningful structure of video and the prior production information. And by the proposed the construction and generation method of the video database implemented the video database which can produce the new contents for the internet broadcasting centralized on the video database. For this production, We proposed the video indexing method which integrates the annotation-based retrieval and the content-based retrieval in order to extract and retrieval the feature information of the video data using the relationship between the meaningful structure and the prior production information on the process of the video parsing and extracting the representative key frame. We can improve the performance of the video contents retrieval, because the integrated video indexing method is using the content-based metadata type represented in the low level of video and the annotation-based metadata type impressed in the high level which is difficult to extract the feature information of the video at he same time.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.96-106
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• 2002

We introduce a novel scheme to characterize and index events in TV golf programs using MPEG-7 descriptors. Our goal is to identify and localize the golf events of interest to facilitate highlight-based video indexing and summarization. In particular, we analyze multiple (low-level) visual features using domain-specific model to create a perceptual relation for semantically meaningful(high-level) event identification. Furthermore, we summarize a TV golf program with TV-Anytime segmentation metadata, a standard form of an XML-based metadata description, in which the golf events are represented by temporally localized segments and segment groups of highlights. Experimental results show that our proposed technique provides reasonable performance for identifying a variety of golf events.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.449-463
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• 2015

Video object tracking is important for variety of applications, such as security, video indexing and retrieval, video surveillance, communication, and compression. This paper proposes an object tracking method in HEVC bitstreams. Without pixel reconstruction, motion vector (MV) and size of prediction unit in the bitstream are employed in an Spatio-Temporal Markov Random Fields (ST-MRF) model which represents the spatial and temporal aspects of the object's motion. Coefficient-based object shape adjustment is proposed to solve the over-segmentation and the error propagation problems caused in other methods. In the experimental results, the proposed method provides on average precision of 86.4%, recall of 79.8% and F-measure of 81.1%. The proposed method achieves an F-measure improvement of up to 9% for over-segmented results in the other method even though it provides only average F-measure improvement of 0.2% with respect to the other method. The total processing time is 5.4ms per frame, allowing the algorithm to be applied in real-time applications.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.