• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.366-381
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• 2013

This paper is to identify how variety of products, product quality, guarantees, employee services and physical environment of store which is considered to store image components influence on satisfaction and loyalty, which in turn effects on loyalty in grocery retailing stores. A survey was conducted to collect the data with consumers who had the actual purchase experience within 1 years in grocery retailing stores. Analysis of structural equation modeling with SPSS 19.0 and AMOS 16.0 were performed to test the research hypothesis. The result of the study as follows: First, product quality and employee services influence on both satisfaction and trust, but physical environment of store are effects on satisfaction only. Second, no store image components influence on loyalty. Finally, satisfaction was effect on both trust and loyalty, whereas trust was not effect on loyalty. In order to build strong customer loyalty, marketer have to strengthen the relationship quality such as satisfaction and trust, and formed through store image components that is much stronger on loyalty.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.235-238
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• 2009

최근 생활수준의 향상으로 의복의 기능성이 중시되면서, 건강, 감성, 쾌적 등에 대한 욕구를 충족시킬 수 있는 건강 소재 개발에 대한 연구와 니트에 관한 선호도 및 감성연구는 활발히 진행되고 있다. 그러나 현재까지 건강 니트 소재의 감각 및 감성이미지에 관한 연구는 부족한 실정이다. 본 연구는 키토산 섬유와 서스 섬유의 니트 소재를 편성한 다음 최종 소비자의 감각과 감성이미지에 미치는 영향을 연구하여 실제 건강 니트 소재를 기획하는데 필요한 정보를 제시하고자 한다. 본 연구에서 키토산 섬유와 서스 섬유를 회색계열로 변화를 주어 10 게이지의 컴퓨터 자동 횡편기로 5 종의 평편 시료를 편성하였고 20 대 남녀 대학생 69 명을 대상으로 5 종의 시료($20\;cm{\times}15\;cm$)를 랜덤한 순서로 제시하여 눈으로 시료를 보고 직접 만지면서 평가하도록 하였으며, 감각 18 개와 감성 22 개, 선호도 3 개의 총 43 개 형용사로 이루어진 7 점 척도를 사용하였다. 건강 니트 소재의 감각 및 감성 이미지를 요인 분석한 결과, 감각요인은 '부피감', '요철감', '신축감', '현시감', 변형감'의 5 가지 요인, 감성요인은 '온유감', '안정감', '고급감', '활동감'의 4 가지 요인으로 분류되었다. 색 속성 중 명도 수준별 감각요인 및 감성요인 중 '요철감'과 '안정감'의 매우 유의한 차이가 나타났다. 고명도, 저명도 수준은 울퉁불퉁하고 오톨도톨하지만 안정적이고 깨끗한 이미지로 느끼는 것으로 나타났고 중간 명도수준은 '요철감'과 '안정감'이 감소되었다. 차콜색의 키토산 100%와 연회씩의 서스 100%의 경우 울퉁불퉁하고 오톨도톨하지만 안정적이고 깨끗한 이미지로 느끼는 것으로 나타났고, 차콜색 키토산섬유와 연회색 서스섬유를 혼방하여 편성한 경우 '요철감'과 '안정감'이 감소되었다.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.1
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• pp.417-435
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• 2016

Relevance judgment is important in terms of improving the effectiveness of information retrieval systems, and it has been dominant for users to search and use images utilizing internet and digital technologies. However, in the field of image retrieval, there have been only a few studies in terms of identifying relevance criteria. The purpose of this study aims to identify and characterize the non-relevance criteria from the failed image searches. In order to achieve the purpose of this study, a total of 135 participants were recruited and a total of 1,452 criteria items were collected for this study. Analyses and identification on the data set found thirteen criteria such as 'topicality', 'visual content', 'accuracy', 'visual feature', 'completeness', 'appeal to user', 'focal point', 'bibliographic information', 'impression', 'posture', 'face feature', 'novelty', and 'time frame'. Among these criteria, 'visual content' and 'focal point' were introduced in this current study, while 'action' criterion identified in previous studies was not shown in this current study. When image needs and image uses are analyzed with these criteria, there are distinctive differences depending on different image needs and uses.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.3
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• pp.297-320
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• 2018

This study aims to improve the access and retrieval of images and to find a way to effectively generate tags as a tool for providing explanation of images. To do this, this study investigated the features of human tagging and machine tagging, and compare and analyze them. Machine tags had the highest general attributes, some specific attributes and visual elements, and few abstract attributes. The general attribute of the human tag was the highest, but the specific attribute was high for the object and scene where the human tag constructor can recognize the name. In addition, sentiments and emotions, as well as subjects of abstract concepts, events, places, time, and relationships are represented by various tags. The tag set generated through this study can be used as basic data for constructing training data set to improve the machine learning algorithm.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.301-315
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• 2013

The purpose of this study is to investigate characteristics of hydromorpological structures that affect landscape preference and visual characteristics on the sections of the designated streams where have dynamic ecological characteristics. We evaluated the ecological status of the streams utilizing LAWA to assess hydromorpological structures of streams. We also investigated preference and visual characteristics of stream landscapes through Semantic Differential Scale(SD scale). The differences of visual images according to the characteristics of hydromorpological structures in the sites were analyzed by descriptive statistics, One-way ANOVA, and t-test. As a result, this study showed that sections represented as "good" ecological status are shown to be harmonious, beautiful, natural, and clean comparing to sections represented as "poor" ecological status. The hydromorpological structures that have significant impacts on the visual characteristics are considered as riparian vegetation, cross-sectional shape, and the artificial structures. Results of this study can help guide the stream restoration of the damaged stream to improving ecological function and positive landscape.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.167-176
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• 2019

This study was conducted to determine the types of nurse's image in nurses and nursing students by using Q-methodology. Total 40 Q-samples were extracted from Q-population consisted of the 385 statements, which were collected from 135 nurses with more than one year clinical experience in H city and 250 nursing students at H university. The Q-sample classification was conducted by 10 nurses and 40 nursing students, and data were analyzed by using PQ method program. As results, the types of nurse's images were derived as 'textbooked', 'realistic' and 'periodic.' This study was meaningful in analyzing the types of nurse image from both nurses and nursing students. In order to improve the image of nurses, we propose not only to develop a systematic nursing curriculum but also to develop health and medical policies reflecting reality of care settings.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.191-204
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• 2021

Visual Question Answering (VQA) and image captioning are tasks that require understanding of the features of images and linguistic features of text. Therefore, co-attention may be the key to both tasks, which can connect image and text. In this paper, we propose a model to achieve high performance for VQA by image caption generated using a pretrained standard transformer model based on MSCOCO dataset. Captions unrelated to the question can rather interfere with answering, so some captions similar to the question were selected to use based on a similarity to the question. In addition, stopwords in the caption could not affect or interfere with answering, so the experiment was conducted after removing stopwords. Experiments were conducted on VQA-v2 data to compare the proposed model with the deep modular co-attention network (MCAN) model, which showed good performance by using co-attention between images and text. As a result, the proposed model outperformed the MCAN model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1486-1494
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• 2021

Character recognition is a technology required in various platforms, such as smart parking and text to speech, and many studies are being conducted to improve its performance through new attempts. However, with low-quality image used for character recognition, a difference in resolution of the training image and test image for character recognition occurs, resulting in poor accuracy. To solve this problem, this paper designed an end-to-end learning neural network that combines image super-resolution and character recognition so that the character recognition model performance is robust against various quality data, and implemented an alternative whole learning algorithm to learn the whole neural network. An alternative end-to-end learning and recognition performance test was conducted using the license plate image among various text images, and the effectiveness of the proposed algorithm was verified with the performance test.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.6
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• pp.185-192
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• 2022

Multimodal medical image fusion (MMIF) fuses two images containing different structural details generated in two different modes into a comprehensive image with saturated information, which can help doctors improve the accuracy of observation and treatment of patients' diseases. Therefore, a method based on double-layer decomposer and fine structure preservation model is proposed. Firstly, a double-layer decomposer is applied to decompose the source images into the energy layers and structure layers, which can preserve details well. Secondly, The structure layer is processed by combining the structure tensor operator (STO) and max-abs. As for the energy layers, a fine structure preservation model is proposed to guide the fusion, further improving the image quality. Finally, the fused image can be achieved by performing an addition operation between the two sub-fused images formed through the fusion rules. Experiments manifest that our method has excellent performance compared with several typical fusion methods.