• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.433-438
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• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of Korean Biological Nursing Science
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• 제16권4호
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• pp.342-348
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• 2014

Purpose: The purpose of this study was to examine the effects of pressure ulcer classification system education on hospital nurses' knowledge and visual discrimination ability of pressure ulcer classification system and incontinence-associated dermatitis. Methods: One group pre- and post-test was used. A convenience sample of 96 nurses participating in pressure ulcer classification system education, were enrolled in single institute. The education program was composed of a 50-minute lecture on pressure ulcer classification system and case-studies. The pressure ulcer classification system and incontinence-associated dermatitis knowledge test and visual discrimination tool, consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 18.0. Results: The overall mean difference of pressure ulcer classification system knowledge (t=4.67, p<.001) and visual discrimination ability (t=10.58, p<.001) were statistically and significantly increased after pressure ulcer classification system education. Conclusion: Overall understanding of pressure ulcer classification system and incontinence-associated dermatitis after pressure ulcer classification system education was increased, but tended to have lack of visual discrimination ability regarding stage III, suspected deep tissue injury. Differentiated continuing education based on clinical practice is needed to improve knowledge and visual discrimination ability for pressure ulcer classification system, and comparison experiment research is required to evaluate its effects.

• 대한원격탐사학회지
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• 제18권3호
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• pp.163-169
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• 2002

The land uses of Korean peninsula are very complicated and high-density. Therefore, the image classification using coarse resolution satellite images may not provide good results for the land cover classification. The purpose of this paper is to compare the classification accuracy of visual interpretation with that of digital image classification of satellite remote sensing data such as 20m SPOT and 30m TM. In this study, hybrid classification was used. Classification accuracy was assessed by comparing each classification result with reference data obtained from KOMPSAT-1 EOC imagery, air photos, and field surveys.

• 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.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• 한국식생활문화학회지
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• 제28권2호
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• pp.177-185
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• 2013

This study analyzed restaurant table settings with Gestalt's law of visual perception to obtain basic data for future marketing strategies. The research uses methods that involve applying images of restaurant table settings to Gestalt's law of visual perception, doing content analysis, and conducting a frequency analysis as well as a Chi-square test for classification analysis by visual perception. Results show a significant difference in the laws of visual perception, especially in the laws of nearness and closure, between table settings of different countries and backgrounds, such as Korean, Japanese, Chinese, Western cultures. In terms of the law of nearness, Chinese dishes were low, while other countries' dishes and Korean dishes showed high figures. In terms of the law of closure, Japanese dishes and western dishes had low values, while other countries' dishes and Korean dishes were high in their closure. Further studies on consumer awareness by visual perception classification need to be conducted.

• 감성과학
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• 제21권1호
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• pp.155-164
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• 2018

온도감은 촉각과 시각에 의해서 느낄 수 있는 감각이다. 하지만 온도감에 대한 개념을 시각적 온도감과 촉각적 온도감의 개념을 함께 활용되는 경우는 드물다. 이 연구에서는 촉각을 통해 느끼는 온도감과 시각을 통해 느끼는 온도감에 대해 색채와 재질 특성에 따른 시각적 온도감의 관계를 알아보았다. 색채와 재질의 특성을 포함할 수 있는 샘플로 원단을 선택하였다. 15-16가지 종류로 Y계열, R계열, B계열, G계열 총 61개의 샘플로 실험을 진행하였다. 분석 방법은 Yellow, Red, Blue, Green의 색을 떠올렸을 때 느끼는 색에 대한 온도감을 분석한 뒤 각 색상별로 원단의 촉각적 분류 및 시각적 분류에 따른 시각적 온도감에 대해 알아보았다. 그리고 무게, 두께, 요철에 따른 시각적 온도감의 상관관계를 알아보았다. 그 결과 동일한 원단에서는 색 온도감에 따라 Cool과 Warm으로 느끼는 원단의 수가 차이가 났다. 그렇지만 원단의 분류마다 시각적 온도감은 다르게 나타났다. 특히, 얇은, 비치는 원단과 무광택 원단에서 두드러지게 나타났다. 무게, 두께, 요철과 온도감의 관계에서는 무게와 관련을 보인 원단 분류는 딱딱한, 무광택 원단의 분류이며, 두께와 관련을 보인 원단 분류는 얇은, 비치는 원단이다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권12호
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• pp.547-552
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• 2014

플리커, 페이스북과 같은 대용량 소셜 미디어 공유 사이트의 발전으로 이미지 정보가 매우 빠르게 증가하고 있다. 이에 따라 소셜 이미지를 정확하게 검색하기 위한 다양한 연구가 활발히 진행되고 있다. 이미지 태그들의 의미적 연관성을 이용하여 태그기반의 이미지 검색의 정확도를 높이고자 하는 연구를 비롯하여 이미지 단어집(Bag of Visual Words)을 기반으로 웹 이미지를 분류하는 연구도 다양하게 진행되고 있다. 본 논문에서는 이미지에서 배경과 같은 중요도가 떨어지는 정보를 제거하여 중요부분을 찾는 GBVS(Graph Based Visual Saliency)모델을 기존 연구에 사용할 것을 제안한다. 제안하는 방법은 첫 번째, 이미지 태그들의 의미적 연관성을 이용해 1차 분류된 데이터베이스에 SIFT알고리즘을 사용하여 이미지 단어집(BoVW)을 만든다. 두 번째, 테스트할 이미지에 GBVS를 통해서 이미지의 관심영역을 선택하여 테스트한다. 의미연관성 태그와 SIFT기반의 이미지 단어집을 사용한 기존의 방법에 GBVS를 적용한 결과 더 높은 정확도를 보임을 확인하였다.

• 패션비즈니스
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• 제21권3호
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• pp.43-57
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• 2017

This study concerns visual humor in fake technique fashion. While previous studies focused mainly on expression techniques of fake technique fashion, this study analyzed visual humor in fake technique fashion based on classification criteria of visual humor expression techniques, differenting this study from other studies. The purpose of this study was to derive visual humor in fake technique fashion by classifying cases of fake technique fashion, and re-classifying outcomes of primary classification based on criteria of visual humor expression techniques. As for methods, this theoretical study was conducted on humor, expression techniques of visual humor, fake fashion and fake expression techniques through literature review. Subsequently, 485 fake technique fashion images obtained from research were classified by expression techniques, and cases of fake technique fashion were analyzed. In addition, by combining this theoretical study with case studies, fake technique fashion was re-classified according to criteria of visual humor expression techniques to derive the characteristics of visual humor in fake technique fashion. Based on visual humor expression techniques, visual humor in fake technique fashion was created by distortion and transformation that made the fake look real by distorting or transforming the fake, enlargement and reduction that created new forms by altering familiar forms, and typeplay that added fun by changing familiar luxury logos into various forms.