• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1540-1549
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• 2004

In this paper, we propose an algorithm to detect semantic events in golf video using Hidden Markov Model. The purpose of this paper is to identify and classify the golf events to facilitate highlight-based video indexing and summarization. In this paper we first define 4 semantic events, and then design HMM model with states made up of each event. We also use 10 multiple visual features based on MPEG-7 visual descriptors to acquire parameters of HMM for each event. Experimental results showed that the proposed algorithm provided reasonable detection performance for identifying a variety of golf events.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1130-1139
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• 1998

This paper presents an integrated solution for the content-based news video indexing and the retrieval. Currently, it is impossible to automatically index a general video, but we can index a specific structural video such as news videos. Our proposed model extracts automatically the key frames by using the structured knowledge of news and consists of the news item segmentation, caption recognition and search browser modules. We present above three modules in the following: the news event segmentation module recognizes an anchor-person shot based on face recognition, and then its news event are divided by the anchor-person's frame information. The caption recognition module detects the caption-frames with the caption characteristics, extracts their character region by the using split-merge method, and then recognizes characters with OCR software. Finally, the search browser module could make a various of searching mechanism possible.

• Journal of the HCI Society of Korea
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• v.9 no.2
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• pp.23-31
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• 2014

A variety of writing support systems focus on the information management or the feature analysis of the commercially successful narrative texts. In these approaches, the reader's role in the narrative creating process is overlooked. During a writing work, an author anticipates the reader's response or expectation to the narrative and he/she organizes the narrative either along or against the prediction about readers. Assessing and controlling the reader's comprehension in the development of events influences the aesthetic quality of the narrative. In this paper, we suggest a writing support system to visualize and adjust the characteristics of a narrative text related to the reader's comprehension, which is theoretically based on the narrative structure model and the event-indexing situation model. Under the development of the support system, we designed an interactive framework to create events as the basic units of story and arrange them onto both story- and discourse-time axes. Using this framework, we analyzed the organization of events about an actual film narrative. We also proposed both the continuity of the situational dimensions and the cognitive complexity as the characteristics to affect the reader's comprehension, hence we devised a method to visualize and evaluate them. This method was applied to the actual film narrative and the result was discussed in the aspect of the features of the narrative and wiring support strategies.

• 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.