• Journal of the Korean Society for information Management
• /
• v.20 no.3
• /
• pp.111-127
• /
• 2003

The purpose of this study is to ascertain whether event tracking is more effective in event retrieval than information filtering. This study examined the two techniques for event retrieval to suggest the more effective one. The event-retrieval performances of the event tracking technique based on a kNN classifier and the query-based information filtering technique were compared. Two event tracking experiments, one with the static training set and the other with the dynamic training set , were carried out. Two information filtering experiments, one with initial queries and the other with refined queries, were also carried out to evaluate the event-retrieval effectiveness. We found that the event tracking technique with the static training set performed better than on with the dynamic training set. It was also found that the information fitering technique using intial queries performed better than one using the refined queries. In conclusion, the comparison of the best cases of event tracking and information filtering revealed that the information filtering technique outperformed the event tracking technique in event retrieval.

• Journal of the Korean Society for Library and Information Science
• /
• v.43 no.3
• /
• pp.163-180
• /
• 2009

In this paper, we describe a developmental system for establishing personal information tendency based on user queries. For each query, the system classified it based on the category information using a kNN classifier. As category information, we used DDC field which is already assigned to each record in the database. The system accumulates category information for all user queries and the user's personalized feature for the target database. We then developed a personalized retrieval system reflecting the personalized feature to produce search result. Our system re-ranks the result documents by adding more weights to the documents for which categories match with the user's personalized feature. By using user's tendency information, the ambiguity problem of the word could be solved. In this paper, we conducted experiments for personalized search and word sense disambiguation (WSD) on a collection of Korean journal articles of science and technology arena. Our experimental result and user's evaluation show that the performance of the personalized search system and WSD is proved to be useful for actual field services.

• MALSORI
• /
• no.46
• /
• pp.37-50
• /
• 2003

In this paper, we evaluate and compare the performance of speech/music discrimination based on various feature parameters and classifiers. As for feature parameters, we consider High Zero Crossing Rate Ratio (HZCRR), Low Short Time Energy Ratio (LSTER), Spectral Flux (SF), Line Spectral Pair (LSP) distance, entropy and dynamism. We also examine three classifiers: k Nearest Neighbor (k-NN), Gaussian Mixure Model (GMM), and Hidden Markov Model (HMM). According to our experiments, LSP distance and phoneme-recognizer-based feature set (entropy and dunamism) show good performance, while performance differences due to different classifiers are not significant. When all the six feature parameters are employed, average speech/music discrimination accuracy up to 96.6% is achieved.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.10
• /
• pp.2707-2715
• /
• 1999

Recently, there have been researches to automate processing and analysing images in the medical field using image processing technique, a fast communication network, and high performance hardware. In this paper, we propose a system to be able to analyze morphological abnormality of red-blood cells for peripheral blood image using image processing techniques. To do this, we segment red-blood cells in the blood image acquired from microscope with CCD camera and then extract UNL fourier features to classify them into 15 classes. We reduce the number of multi-variate features using PCA to construct a more efficient classifier. Our system has the best performance in recognition rate, compared with two other algorithms, LVQ3 and k-NN. So, we show that it can be applied to a pathological guided system.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.44 no.1
• /
• pp.56-63
• /
• 2007

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and con-nected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based tex-ture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a 'aligns games.' The results show that the system process more than 30 frames/sec on PC for the $320{\times}240$ size input image and supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of Broadcast Engineering
• /
• v.9 no.1
• /
• pp.2-7
• /
• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• The Journal of the Acoustical Society of Korea
• /
• v.25 no.6
• /
• pp.269-276
• /
• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.97-117
• /
• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of the Korean Society for Library and Information Science
• /
• v.32 no.4
• /
• pp.105-115
• /
• 1998

In this paper, we propose a document clustering and retrieval tool which allows users to manage their emotion based document access. This system name is ECRAS(Emotion based Clustering and Retrieval Agent System). Our system extract S emotion feature which like HAPPY, SAD, ANGRY, FEAR, DISGUST from various document. And, our system have retrieve documents for user query base on emotion feature.

• The Magazine of the IEIE
• /
• v.21 no.10
• /
• pp.47-54
• /
• 1994

본 논문에서는 물체들의 이동(translation) 축적(scale) 그리고 회전방향(orientation)에 무관하게 물체를 인식하는 실용적인 패턴 인식 시스템을 소개한다. 이 시스템은 2진영상으로 변환하는데 필요한 임계치(threshold)의 큰 변화에도 덜 민감하다. 특징 벡터(feature vector)로 서는 Zernike 모멘트를 사용하였는데 지금까지 잘 알려진 Hu가 제안한 7개의 모멘트 불변수 (moment invariants)와 비교한다. 또한, 실용적인 기계 시각(machine vision) 시스템에 대해 세 가지 중요한 문제로서 패턴 정규화(pattern nomalization), Zernike 모멘트의 신속한 계산, 그리고 k-NN 규칙을 이용한 분류 등을 논의하였다. 실험에서는 임의의 회전 방향에서 문자들의 크기가 10x10 화소(pixel)에서 512x512 화소까지 변하는 서로 다른 크기를 가진 인쇄된 62개의 문자와 숫자 그리고 기호들을 서로 다른 임계치에서 인식하는 것을 보여준다.