• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1464-1485
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• 2021

Emotional style of multimedia art works are abstract content information. This study aims to explore emotional style transfer method and find the possible way of matching music with appropriate images in respect to emotional style. DCNNs (Deep Convolutional Neural Networks) can capture style and provide emotional style transfer iterative solution for affective image generation. Here, we learn the image emotion features via DCNNs and map the affective style on the other images. We set image emotion feature as the style target in this style transfer problem, and held experiments to handle affective image generation of eight emotion categories, including dignified, dreaming, sad, vigorous, soothing, exciting, joyous, and graceful. A user study was conducted to test the synesthesia emotional image style transfer result with ground truth user perception triggered by the music-image pairs' stimuli. The transferred affective image result for music-image emotional synesthesia perception was proved effective according to user study result.

• The Research Journal of the Costume Culture
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• v.18 no.2
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• pp.348-370
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• 2010

The purpose of this study was to propose some plan which could satisfy consumer's expectation emotional needs by comparing emotional scale between fashion brand image and brand website coloration image. For this study, 12 brand websites within four fashion zone, men's clothing, women's clothing, casual wear, and sports wear were chosen. The questionnaires were comprised of 27 emotional adjectives which were selected from previous studies. The questionnaires were distributed to university students and office workers for 3 to 17 on September. Among them, 118 questionnaires were analyzed by SPSS tool. The qualitative analysis for emotional adjective sorting, content analysis for website color chip sorting, and quantitative analysis for consumers were used in this study. Some differences exist between brand image and website coloration band image as the result. As the numbers of internet user became larger, the costumer's emotional image which gives maximum satisfaction is getting more important in fashion brand website. Therefore, fashion website managers should satisfy consumers with functional and emotional needs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.3
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• pp.10-17
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• 2009

In this paper, we explained the relation between evaluating display device and emotional image quality evaluation in human perceptual view. It is also suggested two emotional image quality evaluation method of display reflecting human visual function. One is the color space of CIECAM02 and the other is capturing moving image. It is necessary to standardize the evaluation methods of image quality based on emotional evaluation.

• Culinary science and hospitality research
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• v.14 no.4
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• pp.398-414
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• 2008

The purpose of this study is to analyze the effect of restaurant atmosphere on its image and customers' emotional responses and behavior. The results of this study indicated that perceived restaurant atmospheres had a significant effect on customers' emotional responses, and these emotional responses greatly influenced the image of a restaurant. Especially, the ambient and cleanliness factors of restaurant atmosphere influenced a restaurant image, and the positive image from those factors had a significant effect on customer behavior. The design and human factors of restaurant atmosphere influenced customer behavior, and the positive image from those factors had a significant effect on customer behavior. Finally, it was verified that the restaurant atmospheric factors affected its image and customers' emotional responses and behavior. Moreover, the better the restaurant atmospheric factors(design, ambient, cleanliness, humanity) are, the better customers' emotional responses and image are, thereby increasing customers' revisiting and word-of-mouth intention.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.131-138
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• 2018

In this paper, we have implemented speeches that utilized the emotion information of the user's speech and image matching and recommendation system. To classify the user's emotional information of speech, the emotional information of speech about the user's speech is extracted and classified using the PLP algorithm. After classification, an emotional DB of speech is constructed. Moreover, emotional color and emotional vocabulary through factor analysis are matched to one space in order to classify emotional information of image. And a standardized image recommendation system based on the matching of each keyword with the BM-GA algorithm for the data of the emotional information of speech and emotional information of image according to the more appropriate emotional information of speech of the user. As a result of the performance evaluation, recognition rate of standardized vocabulary in four stages according to speech was 80.48% on average and system user satisfaction was 82.4%. Therefore, it is expected that the classification of images according to the user's speech information will be helpful for the study of emotional exchange between the user and the computer.

• Korean Institute of Interior Design Journal
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• v.20 no.6
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• pp.71-78
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• 2011

Although the recognition and emotion about space is subjective and individual, if standard is proposed through common factor, objective, quantified space image evaluation will be available. In addition, space image evaluation standard caused by recognitive-emotional factor can meet requests of space users and increase psychological satisfactions. The purpose of this study is to grasp the space image caused by recognitive-emotional factor in space with PAD model and analyze the evaluation of space image giving visual, recognitive and emotional effects. The analysis result revealed that 'joyfulness' and access-avoidance had a very similar distribution. The result means that space is evaluated with the degree of 'joyfulness' for space and it is led by approach-avoidance behavior. The recognition factor that forms and evaluates space image and decides approach-avoidance is expressed as adjective images such as 'fresh, joyful, light and static and its emotional factors are adjective images such as 'calm, allowable, joyful and quiet'.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.05b
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• pp.208-209
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• 2002

• The Research Journal of the Costume Culture
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• v.22 no.3
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• pp.399-410
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• 2014

The purpose of this study was to investigate emotional images and preference of knitwear by tone on tone combination. The subjects were 357 university students in Daejeon and Chungnam province, and the measuring instruments were 6 stimuli manipulated by color and tone combination type of background and pattern in the tone and tone combination, and self-administrated questionnaires consisted of emotional images items, preference items, and subjects' demographics attributions. The data were analyzed by Cronbach's ${\alpha}$, factor analysis, t-test, MANOVA and Duncan's multiple range test, using SPSS program. The results were as follows. First, four factors (attractiveness, conspicuity, mildness, and activity) are emerged on emotional images of knitwear. Second, color had main effects on emotional images and preference. Gray color was perceived as most attractive image and more preferred than others. Third, tone combination type had some effects on emotional images. Vivid tone background/light tone pattern was perceived more attractive image but less conspicuous and mild than light tone background/vivid tone pattern. Forth, subjects' gender had an effects on conspicuous image. Male was perceived more conspicuous image on knitwear stimuli than female. Fifth, color and subjects' gender had interaction effects on attractiveness image and preference. Male perceived that blue is more attractive and preferred than female.

• Cartoon and Animation Studies
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• s.39
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• pp.393-412
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• 2015

See and feel the emotion recognition is the image of a person variously changed according to the environment, personal disposition. Thus, the image recognition has been focused on the emotional sensibilities computer you want to control the number studies. However, existing emotional computing model is numbered and the objective is clearly insufficient measurement conditions. Thus, through quantifiable image Emotion Recognition and emotion computing, is a study of the situation requires an objective assessment scheme. In this paper, the sensitivity was represented by numbered sizes quantified according to the image recognition calculation emotion. So apply the principal attributes of the color image emotion recognition as a configuration parameter. In addition, in calculating the color sensitivity by applying a digital computing focused research. Image color emotion computing research approach is the color of emotion attribute, brightness, and saturation reflects the weighted according to importance to the emotional scores. And free-degree by applying the sensitivity point to the image sensitivity formula (X), the tone (Y-axis) is calculated as a number system. There pleasure degree (X-axis), the tension and position the position of the image point that the sensitivity of the emotional coordinate crossing (Y-axis). Image color coordinates by applying the core emotional effect of Russell (Core Affect) is based on the 16 main representatives emotion. Thus, the image recognition sensitivity and compares the number size. Depending on the magnitude of the sensitivity scores demonstrate this sensitivity must change. Compare the way the images are divided up the top five of emotion recognition emotion emotions associated with 16 representatives, and representatives analyzed the concentrated emotion sizes. Future studies are needed emotional computing method of calculation to be more similar sensibility and human emotion recognition.