• ETRI Journal
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• v.27 no.6
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• pp.806-809
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• 2005

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG-7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non-linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personalbased video summarization and indexing in stored video data.

• 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 Korea Multimedia Society
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• v.6 no.5
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• pp.797-804
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• 2003

Scene change detection algorithm, which is very important preprocessing technique for video indexing and retrieval and determines the performance of video database system, is being studied widely. In this paper, we propose a more effective abrupt scene change detection, which is robust to large motion, sudden change of light and successive abrupt shot transitions rapidly. And we also propose a new gradual scene change detection algorithm, which can detect dissolve, and fade in/out precisely. Furthermore, we also propose a representative frame extraction algorithm which performs content-based video summary by novel DCT DC image buffering technique and accumulative histogram intersection measure (AHIM).

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.161-172
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• 1996

The objective of this research is to develope a process of integrating video image into digital map. In order to reach the research objective, the work includes the development of georeferencing technique for video images, the development of pilot system and the assesment process. Georeferencing technique for video images is composed of DGPS positioning, filtering of abnormal points, map conflation, indexing locations for key frames via time tag and indexing locations for total frames. By using the proposed building process, we could find the result that the accuracy of image capturing test points is $92.8%({\pm}2\;frames)$. The eventual meaning of this study is that it is possible to find a new conception of digital map, which overcomes a limitation of exiting two dimensional digital map.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1311-1317
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• 2007

Recently, due to the popularity and explosive growth of the Internet, the information exchange is increasing so rapidly over the Internet. Also the XML is becoming a standard as well as a major tool of data exchange on the Internet and thus we propose the new indexing technique for evaluating a path-oriented query and design and implementation of Path-oriented Query Processing System to give useful for users. In proposed indexing technique, which combined a binary trio structure with a path signature file to improve performance of XML document retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1807-1824
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• 2015

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.735-742
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• 2003

In the information retrieval system, document clustering technique is to provide user convenience and visual effects by rearranging documents according to the specific topics from the retrieved ones. In this paper, we clustered documents using K-Means algorithm and present the effect of index terms weighting scheme on the document clustering. To verify the experiment, we applied Latent Semantic Indexing approach to illustrate the clustering results and analyzed the clustering results in 2-dimensional space. Experimental results showed that in case of applying local weighting, global weighting and normalization factor, the density of clustering is higher than those of similar or same weighting schemes in 2-dimensional space. Especially, the logarithm of local and global weighting is noticeable.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.19-28
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• 1999

Digital image and video libraries require new algorithms for the automated extraction and indexing of salient image features. Eigen values of an image provide one important cue for the discrimination of image content. In this paper we propose a new approach for automated content extraction that allows efficient database searching using eigen values. The algorithm automatically extracts eigen values from the image matrix represented by the covariance matrix for the image. We demonstrate that the eigen values representing shape information and the skewness of its distribution representing complexity provide good performance in image query response time while providing effective discriminability. We present the eigen value extraction and indexing techniques. We test the proposed algorithm of searching by eigen value and its skewness on a database of 100 images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.597-618
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• 2019

Categorical range aggregation, which is conceptually equivalent to running a range aggregation query separately on multiple datasets, returns the query result on each dataset. The challenge is when the number of dataset is as large as hundreds or thousands, it takes a lot of computation time and I/O. In previous work, only a single dimension of the range restriction has been solved, and in practice, more applications are being used to calculate multiple range restriction statistics. We proposed MCRI-Tree, an index structure designed to solve multi-dimensional categorical range aggregation queries, which can utilize main memory to maximize the efficiency of CRA queries. Specifically, the MCRI-Tree answers any query in $O(nk^{n-1})$ I/Os (where n is the number of dimensions, and k denotes the maximum number of pages covered in one dimension among all the n dimensions during a query). The practical efficiency of our technique is demonstrated with extensive experiments.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.1-13
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• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.