• Journal of Information Processing Systems
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• v.9 no.2
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• pp.315-332
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• 2013

This paper presents an approach for improving the use of VP-tree in video indexing and searching. A vantage-point tree or VP-tree is one of the metric space-based indexing methods used in multimedia database searches and data retrieval. Instead of relying on the Euclidean distance as a measure of search space, the proposed approach focuses on the trigonometric inequality for compressing the search range, which thus, improves the search performance. A test result of using 10,000 video files shows that this method reduced the search time by 5-12%, as compared to the existing method that uses the AESA algorithm.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.289-294
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• 2013

Due to sudden transition to intellectual society corresponding with fast technology progress, companies and nations need to focus on development and guarantee of intellectual property. The possession of intellectual property has been the important factor of competition power. In this paper we developed the efficient patent search process with big data analysis tool R. This patent search process consists of 5 steps. We result that at first this process obtain the core patent search key words and search the target patents through search formula using the combination of above patent search key words.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.167-170
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• 2000

In this paper, we present a fast algorithm for the motion estimation using the efficient selection of an initial search position. In the method, we select the initial search position using the motion vector from the subsmpled images, the predicted motion vector from the neighbor blocks, and the (0,0) motion vector. While searching the candidate blocks, we use the spiral search pattern with the successive elimination algorithm(SEA) and the partial distortion elimination(PDE). The experiment results show that the complexity of the proposed algorithm is about 2∼3 times faster than the three-step search(TSS) with the PSNR loss of just 0.05［dB］∼0.1［dB］ than the full search algorithm PSNR. The search complexity can be reduced with quite a few PSNR loss by controling the number of the depth in the spiral search pattern.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.246-247
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• 2010

This paper proposes a fast motion estimation algorithm based on motion speed and multiple initial center points. The proposed method predicts initial search points by means of the spatio-temporal neighboring motion vectors. A dynamic search pattern based on motion speed and the predicted initial center points is proposed to quickly obtain the motion vector. Due to the usage of the spatio-temporal information and the dynamic search pattern, the proposed method greatly accelerates the search speed while maintaining a good predicted image quality. Experimental results show that the proposed method has a good predicted image quality in terms of PSNR with less search time as compared to the Full Search, New Three-Step Search, and Four-Step Search.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.960-968
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• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.389-404
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• 2013

Geospatial information, which is one of social overhead capital, is predicted as a core growing industry for the future. The production of geospatial information requires a huge budget, so it is very important objective of the policy for geospatial information to prevent the duplication of geospatial projects. In this paper, we proposed a semantic search system which extracts possible duplication of geospatial projects by using ontology for geospatial project administration. In order to achieve our goal, we suggested how to construct and utilize geospatial project ontology, and designed the architecture and process of the semantic search. Moreover, we showed how the suggested semantic search works with a duplicated projects search scenario. The suggested system enables a nonprofessional can easily search for duplicated projects, therefore we expect that our research contributes to effective and efficient duplication review process for geospatial projects.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.6-12
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• 2001

Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.418-420
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• 2015

The SNOMED CT browser is a system for searching and browsing of a huge volume of medical terminologies included in SNOMED CT. Previous browsers provide a simple list-up of search results while they are similar from each other. It leads to a serious confusion in selecting an appropriate term among them. This paper presents a novel browser system which provides a comparative search of search results. To do this, the proposed system includes a terminology search module, a topology search module, and a sub-graph generation module for the results.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.47-54
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• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.