• Title/Summary/Keyword: Retrieval simplification

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3D Model Retrieval using Distribution of Interpolated Normal Vectors on Simplified Mesh (간략화된 메쉬에서 보간된 법선 벡터의 분포를 이용한 3차원 모델 검색)

  • Kim, A-Mi;Song, Ju-Whan;Gwun, Ou-Bong
    • Journal of Korea Multimedia Society
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    • v.12 no.11
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    • pp.1692-1700
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    • 2009
  • This paper proposes the direction distribution of surface normal vectors as a feature descriptor of three-dimensional models. Proposed the feature descriptor handles rotation invariance using a principal component analysis(PCA) method, and performs mesh simplification to make it robust and nonsensitive against noise addition. Our method picks samples for the distribution of normal vectors to be proportional to the area of each polygon, applies weight to the normal vectors, and applies interpolation to enhance discrimination so that the information on the surface with less area may be less reflected on composing a feature descriptor. This research measures similarity between models with a L1-norm in the probability density histogram where the distances of feature descriptors are normalized. Experimental results have shown that the proposed method has improved the retrieval performance described in an average normalized modified retrieval rank(ANMRR) by about 17.2% and the retrieval performance described in a quantitative discrimination scale by 9.6%~17.5% as compared to the existing method.

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A Study on Advanced Visualization Methods based on Information Needs Analysis according to NTIS General Users Groups (NTIS 일반 이용자 그룹별 정보 요구 분석에 기반한 정보시각화 개선방안 연구)

  • Nam, Yeon-Hwa;Gang, Ju-Yeon;Kim, Tae-Young;Oh, Hyo-Jung
    • Journal of Korean Library and Information Science Society
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    • v.47 no.1
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    • pp.361-382
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    • 2016
  • NTIS(National Science & Technology Information Service) is the first national R&D information knowledge portal service in the world to provide information of government-funded R&D projects and it has been developed by KISTI. This study investigated several limitations of current NTIS information service, analyzed information needs for end users, and then proposed information recomposition and visualization method to satisfy their information needs. For doing this, we firstly clarified the definition of 'end-users' and classified three different groups of general users. We also discriminated various information needs according to three groups and illustrated some examples of visualization results to meet the requirements - 'retrieval simplification' and 'information visualization'. To consider practicality of our proposed method, we suggested implementation plans to utilize existed NTIS services.

Simplification of Moving Object Trajectory on Road Networks (도로 네트워크 상의 이동 객체 궤적의 간략화)

  • Hwang, Jung-Rae;Kang, Hye-Young;Li, Ki-Joune
    • Journal of Korea Spatial Information System Society
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    • v.9 no.3
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    • pp.51-65
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    • 2007
  • In order to analyze moving object trajectories on road networks, its representation needs to be defined correctly. The most previous methods representing moving object trajectories on road networks defined moving object trajectories as a set of passed location and its time. It is required much time in processing analysis such as retrieval for moving object trajectories. In this paper, we focus on POI(Points of Interest) on road networks and propose methods simplifying moving object trajectories based on it. Our method simplifies moving object trajectories by reducing the number of POIs that moving object trajectories passed and maintains its form after moving object trajectories were simplified.

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Conditional Generative Adversarial Network based Collaborative Filtering Recommendation System (Conditional Generative Adversarial Network(CGAN) 기반 협업 필터링 추천 시스템)

  • Kang, Soyi;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.157-173
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    • 2021
  • With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.