• 제목/요약/키워드: image of science class

검색결과 201건 처리시간 0.034초

Experimental Analysis of Equilibrization in Binary Classification for Non-Image Imbalanced Data Using Wasserstein GAN

  • Wang, Zhi-Yong;Kang, Dae-Ki
    • International Journal of Internet, Broadcasting and Communication
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    • 제11권4호
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    • pp.37-42
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    • 2019
  • In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

러프 집합을 이용한 영상 검색 시스템에 관한 연구 (A Study on Image Retrieval System Using Rough Set)

  • 김영천;김동현;이성주
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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    • pp.479-484
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    • 1998
  • 입력된 영상으로부터 추론된 정보 표를 지식베이스에 저장하여 결정해를 구하는데는 많은 탐색시간이 소비된다. 본 논문에서는 탐색 시간을 감소시키기 위해서 러프집합의 식별(classification)과 근사(approximation) 개념을 이용하여 추론된 정보를 동치 클래스(equivalence class)로 분류하여 간략화한다. 감소된 규칙, 즉 Core와 Reduct 리스트를 구하여 결정해를 검색하는데 탐색 시간을 감소시키는데 있다.

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Tomographic sagittal root position in relation to maxillary anterior bone housing in a Brazilian population

  • Rodrigues, Diogo Moreira;Petersen, Rodrigo Lima;Montez, Caroline;Barboza, Eliane Porto
    • Imaging Science in Dentistry
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    • 제52권1호
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    • pp.75-82
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    • 2022
  • Purpose: This cross-sectional study evaluated and categorized the tomographic sagittal root position (SRP) of the maxillary anterior teeth in a Brazilian population. Materials and Methods: Cone-beam computed tomographic scans of 420 maxillary anterior teeth of 70 patients (35 men and 35 women, mean age 25.2±5.9 years) were evaluated. The SRP was classified as class I, II, III, or IV. In class I, the root is positioned against the buccal cortical plate; in class II, the root is centered in the middle of the alveolar housing; in class III, the root is positioned against the palatal cortical plate; and in class IV, at least two-thirds of the root engage both the buccal and palatal cortical plates. Results: In total, 274 teeth (65.2%) were class I, 39 (9.3%) were class II, 3 (0.7%) were class III, and 104 (24.8%) were class IV. The frequency distribution over the teeth groups was different from the overall analysis. Important differences were found in the frequencies of classes I, II, and IV compared to other populations. Sex was not associated with the SRP classes (P=0.307). Age distribution was significantly different over the classes (P=0.004). Conclusion: The findings of this study on the distribution of SRP classes among the Brazilian population compared to other populations demonstrate that the SRP should be analyzed on a case-by-case basis for an accurate treatment plan in the maxillary anterior area.

Image classification and captioning model considering a CAM-based disagreement loss

  • Yoon, Yeo Chan;Park, So Young;Park, Soo Myoung;Lim, Heuiseok
    • ETRI Journal
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    • 제42권1호
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    • pp.67-77
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    • 2020
  • Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

A Comparative Study of 3D DWT Based Space-borne Image Classification for Differnet Types of Basis Function

  • Yoo, Hee-Young;Lee, Ki-Won;Kwon, Byung-Doo
    • 대한원격탐사학회지
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    • 제24권1호
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    • pp.57-64
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    • 2008
  • In the previous study, the Haar wavelet was used as the sole basis function for the 3D discrete wavelet transform because the number of bands is too small to decompose a remotely sensed image in band direction with other basis functions. However, it is possible to use other basis functions for wavelet decomposition in horizontal and vertical directions because wavelet decomposition is independently performed in each direction. This study aims to classify a high spatial resolution image with the six types of basis function including the Haar function and to compare those results. The other wavelets are more helpful to classify high resolution imagery than the Haar wavelet. In overall accuracy, the Coif4 wavelet has the best result. The improvement of classification accuracy is different depending on the type of class and the type of wavelet. Using the basis functions with long length could be effective for improving accuracy in classification, especially for the classes of small area. This study is expected to be used as fundamental information for selecting optimal basis function according to the data properties in the 3D DWT based image classification.

Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

  • Arezki, Dounia;Fizazi, Hadria
    • Journal of Information Processing Systems
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    • 제17권4호
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    • pp.690-706
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    • 2021
  • Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

Performance of Support Vector Machine for Classifying Land Cover in Optical Satellite Images: A Case Study in Delaware River Port Area

  • Ramayanti, Suci;Kim, Bong Chan;Park, Sungjae;Lee, Chang-Wook
    • 대한원격탐사학회지
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    • 제38권6_4호
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    • pp.1911-1923
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    • 2022
  • The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

An Improved method of Two Stage Linear Discriminant Analysis

  • Chen, Yarui;Tao, Xin;Xiong, Congcong;Yang, Jucheng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권3호
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    • pp.1243-1263
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    • 2018
  • The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

초, 중, 고등학생들의 과학 교사 자질에 대한 이미지 (Images of Competencies of Science Teachers in Elementary and Secondary School Students)

  • 김영신;조윤정;임수민
    • 과학교육연구지
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    • 제44권1호
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    • pp.61-73
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    • 2020
  • 교육의 질을 결정하는 주요 요소 중에서 가장 핵심적인 역할을 하는 것은 교사이다. 그러므로 교육의 질을 높이기 위해서는 교사의 질을 향상시켜야 한다. 교사의 자질은 교직에서 요구되는 기능 즉, 교수 활동 뿐 아니라 생활지도, 학급경영을 수행할 수 있는 능력을 의미한다. 본 연구의 목적은 초, 중, 고등학생들이 원하는 과학 교사의 자질을 분석하고자 하는 것이다. 이를 위하여 초, 중, 고 학생 332명을 대상으로 선호하는 과학 교사의 자질과 기피하는 과학 교사의 자질을 개방형으로 기술하도록 하였다. 그 결과 얻어진 개념들을 언어 네트워크 분석법으로 분석하였다. 이 연구의 결론은 1) 학생들은 선호하는 과학 교사의 자질은 다양한 것으로 나타났다. 이는 학생들이 과학 교사를 긍정적으로 생각하는 면이 많은 것을 의미한다. 또한 학생들에게 다양한 면에서 긍정적인 또는 선호하는 교사의 자질을 보여 줄 수 있다는 것이다. 2) 학생들은 실험을 통해서 과학 현상과 이론, 개념을 이해하고 설명해주길 바라는 것으로 나타났다. 과학 수업에서 학생들은 직접적인 활동이나 체험을 선호하였다. 3) 학생들은 과학 교사의 자질에서 수업과 관련된 내용을 중요시하고 있다. 과학 교수 학습 방법의 개선과 학생들을 긍정적으로 대함으로써 학생들의 과학 교사 나아가 과학에 대한 이미지를 높여야 할 것이다. 이 연구 결과를 기반으로 하여 과학 교사의 구체적인 교수 학습 방법에 따른 이미지 연구가 추가적으로 이루어지길 기대한다.

로하스와 노메딕 소비자층을 위한 외식산업에서의 컬러와 이미지 마케팅에 관한 연구 (A Study on Development of Color and Image Marketing Strategies for the LOHAS & Nomadic Consumer in Foodservice Industry)

  • 장혜진;김윤성
    • 한국조리학회지
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    • 제10권4호
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    • pp.50-66
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    • 2004
  • We defined life style as something that every members of society have in common. These social and cultural environments build up not only society group or every individual's expectation but also its own life style. In that way, these social and cultural environments leads to particular consumer behavior pattern in this food-service industry. So we regard next generation's trend which consists of rational consumers as important indicator when we make future's plan in foodservice industry. We consider smart map which needs rational and continuous consume pattern as the construction of next generation's main consumer class. Therefore, this study tried to develop of color and image marketing strategies to attract LOHAS and nomadic consumer.

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