• Journal of KIISE
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• v.43 no.6
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• pp.686-691
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• 2016

In this paper, we introduced the adaptive spectral co-clustering, a spectral clustering for multiview data, especially data with more than three views. In the adaptive spectral co-clustering, the performance is improved by sharing information from diverse views. For the efficiency in information sharing, a co-training approach is adopted. In the co-training step, a set of parameters are estimated to make all views in data maximally independent, and then, information is shared with respect to estimated parameters. This co-training step increases the efficiency of information sharing comparing with ordinary feature concatenation and co-training methods that assume the independence among views. The adaptive spectral co-clustering was evaluated with synthetic dataset and multi lingual document dataset. The experimental results indicated the efficiency of the adaptive spectral co-clustering with the performances in every iterations and similarity matrix generated with information sharing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.57-63
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• 2009

The rapid growth of computer-based image database, necessity of a system that can manage an image information is increasing. This paper presents a region-based image retrieval method using the combination of color(autocorrelogram), texture(CWT moments) and shape(Hu invariant moments) features. As a color feature, a color autocorrelogram is chosen by extracting from the hue and saturation components of a color image(HSV). As a texture, shape and position feature are extracted from the value component. For efficient similarity confutation, the extracted features(color autocorrelogram, Hu invariant moments, and CWT moments) are combined and then precision and recall are measured. Experiment results for Corel and VisTex DBs show that the proposed image retrieval algorithm has 94.8% Precision, 90.7% recall and can successfully apply to image retrieval system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.42-48
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• 2006

In this paper a joint detection method for other cell interference cancellation is proposed in the next generation hybrid TD-CDMA mobile communication systems. A joint detection technique, a most characteristic feature of hybrid TD-CDMA mobile communication systems. retrieves users' data in the same time slot simultaneously with the elimination of multiple user interference. Previously a two stage joint detection method was proposed to cancel other cell interference as well as multiple user interference in the target cell. However the previous scheme does not have concrete ways to recognize other cell users who give major interference to the target cell. Thus all users in neighbor other cells has to be jointly detected and it causes huge complexity of the two stage joint detection. In this paper a method is proposed to perform two stage joint detection according to users' interference with the target cell. Performances of the proposed scheme are investigated through simulations and compared to the previous method the proposed method has no performance degradation and also lower the complexity of two stage joint detection significantly.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.133-145
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• 2004

Combining multiple classifiers to obtain improved performance over the individual classifier has been a widely used technique. The task of constructing a multiple classifier system(MCS) contains two different issues : how to generate a diverse set of base-level classifiers and how to combine their predictions. In this paper, we review the characteristics of the existing multiple classifier systems: bagging, boosting, and stacking. And then we propose new MCSs: stacked bagging, stacked boosting, bagged stacking, and boasted stacking. These MCSs are a sort of hybrid MCSs that combine advantageous characteristics of the existing ones. In order to evaluate the performance of the proposed schemes, we conducted experiments with nine different real-world datasets from UCI KDD archive. The result of experiments showed the superiority of our hybrid MCSs, especially bagged stacking and boosted stacking, over the existing ones.

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.545-554
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• 2004

Combining multiple classifiers to obtain improved performance over the individual classifier has been a widely used technique. The task of constructing a multiple classifier system(MCS) contains two different Issues how to generate a diverse set of base-level classifiers and how to combine their predictions. In this paper, we review the characteristics of existing multiple classifier systems : Bagging, Boosting, and Slaking. For document classification, we propose new MCSs such as Stacked Bagging, Stacked Boosting, Bagged Stacking, Boosted Stacking. These MCSs are a sort of hybrid MCSs that combine advantages of existing MCSs such as Bugging, Boosting, and Stacking. We conducted some experiments of document classification to evaluate the performances of the proposed schemes on MEDLINE, Usenet news, and Web document collections. The result of experiments demonstrate the superiority of our hybrid MCSs over the existing ones.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.596-598
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• 2010

본 논문에서는 의료영상 중 X-ray 영상을 대상으로 영상을 분류하고 분류 결과에 따라 다중 키워드를 생성하는 방법을 제시한다. X-ray영상은 대부분 그레이 영상임으로 Local Binary Patterns (LBP)을 이용하여 픽셀간의 연관성을 특징으로 추출하고, 실시간 학습 및 분류가 가능한 Random Forests 분류기로 영상들을 30개의 클래스로 분류한다. 또한, 미리 정의된 신체 부위간의 관계 가중치를 분류 스코어에 결합하여 신뢰값을 생성하고 이를 기반으로 영상에 대해 다중 주석을 부여하게 된다. 이렇게 부여된 다중 주석은 키워드 기반의 의료영상을 가능케 함으로 보다 쉽고 효율적인 검색 환경을 제공할 수 있다.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.145-162
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• 2005

To improve the accuracy of land-cover discrimination in SAB data classification, this paper presents a methodology that includes feature extraction and fusion steps with multi-temporal SAR data. Three features including average backscattering coefficient, temporal variability and coherence are extracted from multi-temporal SAR data by considering the temporal behaviors of backscattering characteristics of SAR sensors. Dempster-Shafer theory of evidence(D-S theory) and fuzzy logic are applied to effectively integrate those features. Especially, a feature-driven heuristic approach to mass function assignment in D-S theory is applied and various fuzzy combination operators are tested in fuzzy logic fusion. As experimental results on a multi-temporal Radarsat-1 data set, the features considered in this paper could provide complementary information and thus effectively discriminated water, paddy and urban areas. However, it was difficult to discriminate forest and dry fields. From an information fusion methodological point of view, the D-S theory and fuzzy combination operators except the fuzzy Max and Algebraic Sum operators showed similar land-cover accuracy statistics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.148-151
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• 2006

본 논문에서 얼굴과 홍채 데이터를 사용하여 다중생체시스템에 기반한 스테가노그라피 구현을 제안한다. 이를 위해, 얼굴과 홍채 인식 기반의 다중생체인식을 구성하였다. 여기서, 홍채의 특징벡터는 디지털 워터마킹 기법을 이용하여 얼굴 이미지 안에 숨기게 된다. 얼굴과 홍채의 인식시스템은 퍼지집합 이론과 LDA 기법이 결합하여 확장한 Fuzzy-LDA(Fuzzy-Based Linear Discriminant Analysis)기법을 제안한다. 최종적으로 디지털 워터마킹 기법을 적용하여 얼굴이미지 안에 홍채 정보를 삽입하고 얼굴 데이터와 홍채 데이터를 통한 다중생체인식을 구성하였으며, 최종적으로 생체데이터 인식율의 ROC 곡선을 통해 제안된 워터마킹 기법의 좋은 성능을 확인하였고, 얼굴 인식율을 통해 워터마킹된 얼굴 영상과 원본 얼굴 영상을 비교하였다. 다양한 실험을 통해 제안된 기법이 다중생체시스템을 보호하고 효과적으로 사용 될 수 있음을 확인 할 수 있다.