• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.371-377
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• 2008

In this paper, we present a framework for vision-based two-arm gesture recognition. To capture the motion information of the hands, we perform color-based tracking algorithm using adaptive kernel for each frame. And a feature selection algorithm is performed to classify the motion information into four different phrases. By using gesture phrase information, we build a gesture model which consists of a probability of the symbols and a symbol sequence which is learned from the longest common subsequence. Finally, we present a similarity measurement for two-arm gesture recognition by using the proposed gesture models. In the experimental results, we show the efficiency of the proposed feature selection method, and the simplicity and the robustness of the recognition algorithm.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.94-101
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• 2007

We introduce the way to detect the mutation worm. We implemented the program that can generate signature using LCSeq(Longest Common Subsequence) technique in Suffix Tree studied as pattern recognition algorithm. We also showed the process to detect the mutation of CodeRed worm and Nimda worm and evaluated signatures generated by snort and LCSeq.

• Journal of IKEEE
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• v.13 no.2
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• pp.167-174
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• 2009

Music evokes human emotions or creates music moods through various low-level musical features. Typical music clip consists of one or more moods and this can be used as an important criteria for determining the similarity between music clips. In this paper, we propose a new music retrieval scheme based on the mood change patterns of music clips. For this, we first divide music clips into segments based on low level musical features. Then, we apply K-means clustering algorithm for grouping them into clusters with similar features. By assigning a unique mood symbol for each cluster, we can represent each music clip by a sequence of mood symbols. Finally, to estimate the similarity of music clips, we measure the similarity of their musical mood sequence using the Longest Common Subsequence (LCS) algorithm. To evaluate the performance of our scheme, we carried out various experiments and measured the user evaluation. We report some of the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4491-4509
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• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.187-194
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• 2007

A local alignment algorithm does comparing two strings and finding a substring pair with size l and similarity s. To find a pair with both sufficient size and high similarity, existing normalization approaches maximize the ratio of the similarity to the size. In this paper, we introduce normalization by functions that maximizes f(s)/g(l), where f and g are non-decreasing functions. These functions, f and g, are determined by experiments comparing DNA sequences. In the experiments, our normalization by functions finds appropriate local alignments. For the previous algorithm, which evaluates the similarity by using the longest common subsequence, we show that the algorithm can also maximize the score normalized by functions, f(s)/g(l) without loss of time.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.305-310
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• 2010

In this paper, we propose a heuristic algorithm for motion correspondence based on a point series similarity. A point series is a sequence of points which are sorted in the ascending order of their x-coordinate values. The proposed algorithm clusters the points of a previous frame based on their local adjacency. For each group, we construct several potential point series by permuting the points in it, each of which is compared to the point series of the following frame in order to match the set of points through their similarity based on a proximity constraint. The longest common subsequence between two point series is used as global information to resolve the local ambiguity. Experimental results show an accuracy of more than 90% on two image sequences from the PETS 2009 and the CAVIAR data sets.

• Journal of KIISE
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• v.43 no.10
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• pp.1173-1178
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• 2016

Since the introduction of BitTorrent protocol in 2001, everything can be downloaded through file sharing, including music, movies and software. As a result, the copyright holder suffers from illegal sharing of copyright content. In order to solve this problem, countries have enacted illegal share related law; and internet service providers block pirate sites. However, illegal sites such as pirate bay easily reopen the site by changing the domain name. Thus, we propose a technique to easily detect pirate sites that are reopened. This automated technique collects the domain names using the google search engine, and measures similarity using Longest Common Subsequence (LCS) algorithm by comparing the tag structure of the source web page and reopened web page. For evaluation, we colledted 2,383 domains from google search. Experimental results indicated detection of a total of 44 pirate sites for collected domains when applying LCS algorithm. In addition, this technique detected 23 pirate sites for 805 domains when applied to foreign pirate sites. This experiment facilitated easy detection of the reopened pirate sites using an automated detection system.

• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.19-30
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• 2009

The service that has recently come into the spotlight utilizes the map to first approach the map and then provide various mash-up formed results through the interface. This service can provide precise information to the users but the map is barely reusable. The sketch-map system of this paper, unlike the existing large map system, uses the method of presenting the specific spot and route in XML document and then clustering among sketch-maps. The map service system is designed to show the optimum route to the destination in a simple outline map. It is done by renovating the spot presented by the map into optimum contents. This service system, through the process of analyzing, splitting and clustering of the sketch-map's XML document input, creates a valid form of a sketch-map. It uses the LCS(Longest Common Subsequence) algorithm for splitting and merging sketch-map in the process of query. In addition, the simulation of this system's expected effects is provided. It shows how the maps that share information and knowledge assemble to form a large map and thus presents the system's ability and role as a new research portal.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1213-1228
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• 2017

The pitch tracking of music has been researched for several decades. Several possible improvements are available for creating a good t-distribution, using the instantaneous robust algorithm for pitch tracking framework to perfectly detect pitch. This article shows how to detect the pitch of music utilizing an improved detection method which applies a statistical method; this approach uses a pitch track, or a sequence of frequency bin numbers. This sequence is used to create an index that offers useful features for comparing similar songs. The pitch frequency spectrum is extracted using a modified instantaneous robust algorithm for pitch tracking (IRAPT) as a base combined with the statistical method. The pitch detection algorithm was implemented, and the percentage of performance matching in Thai classical music was assessed in order to test the accuracy of the algorithm. We used the longest common subsequence to compare the similarities in pitch sequence alignments in the music. The experimental results of this research show that the accuracy of retrieval of Thai classical music using the t-distribution of instantaneous robust algorithm for pitch tracking (t-IRAPT) is 99.01%, and is in the top five ranking, with the shortest query sample being five seconds long.