• The Research Journal of the Costume Culture
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• v.24 no.1
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• pp.93-106
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• 2016

Study objectives are: 1) to investigate the difference in consumer perceptions of the model's image and physical attractiveness according to advertising model types; 2) to explore the effect of the difference between the model's image and the consumer's self image, the difference between the model's image and the brand's image, and the physical attractiveness of the model on attitude toward the advertising model; and 3) to explore the effect of attitude toward the advertising model on attitude toward the advertisement. A total of 306 female consumers over the age of 45 participated in experiments with advertisement stimuli for a senior apparel brand. Results showed a significant difference in the model's images and physical attractiveness according to each model type. The consumer's attitude toward the advertising model was determined by physical attractiveness of the model, not by the difference between model's image and the consumers' self-image, nor by the difference between the model's image and brand image. Attitude toward advertisements was determined by attitude toward the advertising model. The findings imply that advertising models of a senior apparel brand can be selected based on the physical attractiveness of the model. Consumers do not consider whether the model's image fits well with their self-images or the brand's image when building an attitude toward the advertising model, and this precedes the consumer's attitude toward the advertisement. These results can be used as guidelines to select appropriate models for advertisements of senior apparel brands.

• Journal of Fashion Business
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• v.9 no.4
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• pp.161-169
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• 2005

The purpose of this study was to examine effectiveness of congruence between brand image and advertisement model on fashion advertisement effect. 206 female college students were surveyed for this study. For this study, three hypothesis were set up as follows: First, if fashion brand image and advertisement model image are in congruence, consumers' product preference would be higher, compared to in disharmony. Second, if fashion brand image and advertisement model image are in congruence, consumers' advertisement attitudes would be higher, compared to in disharmony. Third, if fashion brand image and advertisement model image are in congruence, consumers' purchasing intention would be higher, compared to in disharmony. As the results, three all hypothesis were accepted. Based on these results, fashion marketing strategies regarding advertisement would be suggested.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Journal of Fashion Business
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• v.21 no.6
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• pp.1-15
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• 2017

This study examines the various responses of Chinese consumers, depending on the nationality of the fashion advertising model featured in an advertisement; it explores the effects of a congruence between self-image and advertising image (CSIAI) on consumer attitudes. This study was conducted by collecting data online; 200 samples selecting a Korean model and 200 samples selecting a Chinese model with a fashion brand were analyzed. A structural equation model confirms the conceptual framework for the influence of CSIAI on consumer attitudes and purchase intentions. The results show that the perceived CSIAI of consumers positively influences their attitude toward the advertising and the brand, and further, that advertising and brand attitudes significantly affect the purchase intention of consumers. This positive relationship is moderated by the nationality of the model. These findings suggest that the nationality of the model can serve as an important retail mix for global marketers. Other results and management implications are also discussed.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.71-76
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• 2020

In this paper, we propose a super-resolution method that reconstructs compressed low-resolution images into high-resolution images. We propose a CNN model with a small number of parameters, and even if quantization is applied to the proposed model, super-resolution can be implemented without deteriorating the image quality. To further improve the quality of the compressed low-resolution image, a new degradation model was proposed instead of the existing bicubic degradation model. The proposed degradation model is used only in the training process and can be applied by changing only the parameter values to the original CNN model. In the super-resolution image applying the proposed degradation model, visual artifacts caused by image compression were effectively removed. As a result, our proposed method generates higher PSNR values at compressed images and shows better visual quality, compared to conventional CNN-based SR methods.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.8-15
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• 2018

This paper proposes a convolutional neural network model for distinguishing areas occupied by obstacles from a LiDAR image converted from a 3D point cloud. The channels of a LiDAR image used as input consist of the distances to 3D points, the reflectivities of 3D points, and the heights of 3D points from the ground. The proposed model uses a LiDAR image as an input and outputs a result of a segmented LiDAR image. The proposed model adopts refinement modules with skip connections to segment a LiDAR image. The refinement modules with skip connections in the proposed model make it possible to construct a complex structure with a small number of parameters than a convolutional neural network model with a linear structure. Using the proposed model, it is possible to distinguish areas in a LiDAR image occupied by obstacles such as vehicles, pedestrians, and bicyclists. The proposed model can be applied to recognize surrounding obstacles and to search for safe paths.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.465-473
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• 2023

An image enhancement algorithm and image defogging method are studied in this paper. The formation of fog and the characteristics of fog image are analyzed, and the fog image is preprocessed by histogram equalization method; then the additive white noise is removed by foggy image attenuation model, the atmospheric scattering physical model is constructed, the image detail characteristics are enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image. When the number of training iterations is 3,000, the peak signal-to-noise ratio of the proposed method is 43.29 dB and the image structure similarity is 0.9616, indicating excellent image defogging effect.

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.25-40
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• 2021

Purpose: Marketing communication with customers is essential for companies to survive and grow in a fiercely competitive environment. Advertising is one of the most readily accepted marketing communications by consumers. Unlike a company that owns a distribution network, advertising is vital for a franchise. In general, many advertising models select celebrities. Celebrities are more likely to attract audience attention and influence consumers' purchase intentions. Customers satisfied with advertising are more likely to stay loyal and buy again when the company launches a new product. In addition, customers form a brand image through advertisements. Therefore, in this study, the effect of the characteristics of the foodservice franchise advertising model characteristics on model satisfaction, brand image, and purchase intention. Research design, data, and methodology: The survey of this study was conducted among consumers over the age of 20 who had seen a restaurant franchise advertisement and visited a store within the last 2 months. 305 copies were collected for 7 days during the survey period, from October 21 to October 27, 2021. Among the collected questionnaires, 12 insincere questionnaires were excluded, and 293 were used for analysis. SPSS 25.0 and Smart PLS 3.0 were used as data collected to test the hypothesis. Result: As a result of the study, among the advertising model characteristics of a foodservice franchise, reliability, attractiveness, expertise, and closeness all had a significant positive (+) effect on model satisfaction. In addition, reliability and closeness were found to have a significantly positive (+) effect on brand satisfaction, but attractive and expertise did not significantly affect brand image. Finally, model satisfaction was found to have a significant positive (+) effect on brand image and purchase intention. Brand image also appeared to have a significant positive effect on purchase intention. Conclusions: Research Results First, this study verified the effect of a restaurant franchise advertising model using celebrities by using the characteristics of the advertising model. Second, this study developed a conceptual structure for model characteristics - model satisfaction - brand image - purchase intention. Third, the restaurant franchise marketer needs to select a model in consideration of the privacy of the advertising model. Fourth, consumers tend to equate advertising models with advertising

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.