• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1855-1858
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• 2003

In this paper, we propose image retrieval method more accurate and efficient than the conventional one. First of ail, we perform a shot detection and key frame extraction from the DC image constructed by DCT DC coefficients in the compressed video stream that is video compression standard such as MPEG[I][2]. We get principal axis applying PCA(Principal Component Analysis) to key frames for obtaining indexing information, and divide a domain. Video retrieval uses indexing information of high dimension. We apply KD-Trees(K Dimensional-Trees)[3] which shows efficient retrieval in data set of high dimension to video retrieval method. The proposed method can represent property of images more efficiently and property of domains more accurately using KD-Trees.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.331-339
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• 2004

XML has got to be a new standard for data representation and exchanging by its many good points, and the core part of many new researches and emerging technologies. However, the self-describing characteristic, which is one of XML's good points, caused the spreading of XML documents with different structures, and so the need of the research for the effective XML-document search has been proposed. This paper is for the analysis of the problem in BitCube, which is a bitmap indexing that shows high performance grounded on its fast retrieval. In addition, to resolve the problem of BitCube, we did design and implement xPlaneb(XML Plane Web) which it a new 3-dimensional bitmap indexing made of linked lists. We propose an effective information retrieval technique by replacing BitCube operations with new ones and reconstructing 3-dimensional array index of BitCube with effective nodes. Performance evaluation shows that the proposed technique is better than BitCube, as the amount of document increases, in terms of memory consumptions and operation speed.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.1-12
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• 2003

Recently, there have been many indexing schemes for multimedia data such as image, video data. But recent database applications, for example data mining and multimedia database, are required to support multi-user environment. In order for indexing schemes to be useful in multi-user environment, a concurrency control algorithm is required to handle it. So we propose a concurrency control algorithm that can be applied to CBF (cell-based filtering method), which uses the signature of the cell for alleviating the dimensional curse problem. In addition, we extend the SHORE storage system of Wisconsin university in order to handle high-dimensional data. This extended SHORE storage system provides conventional storage manager functions, guarantees the integrity of high-dimensional data and is flexible to the large scale of feature vectors for preventing the usage of large main memory. Finally, we implement the web-based image retrieval system by using the extended SHORE storage system. The key feature of this system is platform-independent access to the high-dimensional data as well as functionality of efficient content-based queries. Lastly. We evaluate an average response time of point query, range query and k-nearest query in terms of the number of threads.

• Journal of the Korean Ceramic Society
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• v.44 no.4 s.299
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• pp.202-207
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• 2007

High-Quality 1-Dimensional InN single crystalline have been grown by Halide Vapor-Phase Epitaxy on the Au catalyst coated Si substrate using the vapor-liquid-solid growth mechanism. We have been grown 1-dimension InN nanowires having controlled the growth conditions for substrate temperature and gases flow rate. The grown InN nanowire of characteristics for morphologies, crystal structure, and element analysis were carried out by SEM, HR-TEM, and EDS respectively. And the defects of InN crystalline were analyzed by indexing of selective area diffraction pattern with attached HR-TEM. We have successfully obtained the defect-free 1-dimensional InN single crystalline nanowire at the atmosphere pressure.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.1-10
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• 2007

This paper proposes a new indexing mechanism for MPEG-7 visual descriptors, especially Dominant Color and Contour Shape descriptors, that guarantees an efficient similarity search for the multimedia database whose visual meta-data are represented with MPEG-7. Since the similarity metric used in the Dominant Color descriptor is based on Gaussian mixture model, the descriptor itself could be transform into a color histogram in which the distribution of the color values follows the Gauss distribution. Then, the transformed Dominant Color descriptor (i.e., the color histogram) is indexed in the proposed indexing mechanism. For the indexing of Contour Shape descriptor, we have used a two-pass algorithm. That is, in the first pass, since the similarity of two shapes could be roughly measured with the global parameters such as eccentricity and circularity used in Contour shape descriptor, the dissimilar image objects could be excluded with these global parameters first. Then, the similarities between the query and remaining image objects are measured with the peak parameters of Contour Shape descriptor. This two-pass approach helps to reduce the computational resources to measure the similarity of image objects using Contour Shape descriptor. This paper also proposes two integration schemes of visual descriptors for an efficient retrieval of multimedia database. The one is to use the weight of descriptor as a yardstick to determine the number of selected similar image objects with respect to that descriptor, and the other is to use the weight as the degree of importance of the descriptor in the global similarity measurement. Experimental results show that the proposed indexing and integration schemes produce a remarkable speed-up comparing to the exact similarity search, although there are some losses in the accuracy because of the approximated computation in indexing. The proposed schemes could be used to build a multimedia database represented in MPEG-7 that guarantees an efficient retrieval.

• Journal of KIISE:Databases
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• v.28 no.2
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• pp.204-216
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• 2001

최근 이미지 특징 벡터와 같은 고차원 벡터 데이터에 관한 색인 기법들이 많이 연구되고 있다. 하지만, 기존의 색인 기법들은 저차원의 데이터에 대해서는 검색 성능이 우수하지만, 차원이 증가함에 따라 검색 성능이 급격히 저하되는 'dimensional curse' 문제를 안고 있다. 따라서, 본 논문에서는 이러한 문제점을 최소화하기 위해 필터링을 이용한 새로운 색인 기법을 제안한다. 제안하는 셀 기반 필터링 기법은 셀 중심에서 객체까지의 거리값을 저장하여 필터링 효과를 증대시킨다. 또한 고차원 공간을 셀 단위로 분할하며, 각각의 셀을 시그니쳐로 표현한다. 검색을 수행하기 위해, 셀 기반 필터링 기법은 데이터 파일을 직접 접근하기 전에 전체 시그니쳐들을 탐색하여 필터링을 수행함으로써 후보 셀들을 얻는다. 성능 실험을 통해 제안하는 기법이 VA-파일보다 검색 시간에 있어서 약 20%의 성능 향상을 보인다.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.320-335
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• 2003

Location-Based Services(LBS) give rise to location-based queries of which results depend on the locations of moving objects. One of important applications of LBS is to examine tracks of continuously moving objects. Moving objects databases need to provide 3-dimensional indexing for efficiently processing range queries on the movement of continuously changing positions. An extension of the 2-dimensional R-tree to include time dimension shows low space utilization and poor search performance, because of high overlap of index nodes and their dead space. To solve these problems, we propose a new R-tree based indexing technique, namely TR-tree. To increase storage utilization, we assign more entries to the past node by using the unbalanced splitting policy. If two nodes are highly overlapped, these nodes are forcibly merged. It is the forced merging policy that reduces the dead space and the overlap of nodes. Since big line segments can also affect the overlap of index nodes to be increased, big line segments should be clipped by the clipping policy when splitting overfull nodes. The TR-tree outperforms the 3DR-tree and TB-tree in all experiments. Particularly, the storage utilization of the TR-tree is higher than the R-tree and R*-tree.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.145-153
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• 2001

The Pyramid-Technique is based on mapping n-dimensional space data into one-dimensional data and expressing it as B-tree ; and by solving the problem of search time complexity the pyramid technique also prevents the effect \"phenomenon of dimensional curse\" which is caused by treatment of hypercube range query in n-dimensional data space. The Spherical Pyramid-Technique applies the pyramid method’s space division strategy, uses spherical range query and improves the search performance to make it suitable for similarity search. However, depending on the size of data and change in dimensions, the two above technique demonstrate significantly inferior search performance for data sizes greater than one million and dimensions greater than sixteen. In this paper, we propose a new index-structured PdR-Tree to improve the search performance for high dimensional data such as multimedia data. Test results using simulation data as well as real data demonstrate that PdR-Tree surpasses both the Pyramid-Technique and Spherical Pyramid-Technique in terms of search performance.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.455-462
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• 2006

Recently, the filtering approach using vector approximation such as VA-file[1] or LPC-file[2] have been proposed to support similarity search in high dimensional data space. This approach filters out many irrelevant vectors by calculating the approximate distance from a query vector using the compact approximations of vectors in database. Accordingly, the total elapsed time for similarity search is reduced because the disk I/O time is eliminated by reading the compact approximations instead of original vectors. However, the search time of the VA-file or LPC-file is not much lessened compared to the brute-force search because it requires a lot of computations for calculating the approximate distance. This paper proposes a new bitmap index structure in order to minimize the calculating time. To improve the calculating speed, a specific value of an object is saved in a bit pattern that shows a spatial position of the feature vector on a data space, and the calculation for a distance between objects is performed by the XOR bit calculation that is much faster than the real vector calculation. According to the experiment, the method that this paper suggests has shortened the total searching time to the extent of about one fourth of the sequential searching time, and to the utmost two times of the existing methods by shortening the great deal of calculating time, although this method has a longer data reading time compared to the existing vector approximation based approach. Consequently, it can be confirmed that we can improve even more the searching performance by shortening the calculating time for filtering of the existing vector approximation methods when the database speed is fast enough.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.69-77
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• 2009

Stream service environment demands real-time query processing for voluminous data which are ceaselessly delivered from tremendous sources. Typical R-tree based query processing technologies cannot efficiently handle such situations, which require repetitive and inefficient exploration from the tree root on every data event. However, many stream data including sensor readings show high locality, which we exploit to reduce the search space of queries to explore. In this paper, we propose a query processing scheme exploiting the locality of stream data. From the simulation, we conclude that the proposed scheme performs much better than the traditional ones in terms of scalability and exploration efficiency.