• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.11
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• pp.1558-1569
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• 2002

Two components of brand image are brand association related to brand properties and brand persona which is measured by descriptive words. The purpose of this study is to analyze the brand image of apparel by qualitative approach including natural grouping method suggested by Aaker. For this, face-to-face interviews were carried out in March and April 200l.11 interviewees were respectively asked to classify' pre-selected several tens of apparel brands based on their image differentiation, and then to explain the reason of grouping and to describe resultant brand groups. In this process, many brand image associations and brand persona-descriptive words were collected. 9 types of brand association were identified, and these were summarized as three factors suggested by Keller -attributes, benefits, and attitudes/evaluations. And 3 words which used to refer brand image frequently -dokteukhan (unique), simple, and yeosungseureowoon (feminine)- were interpreted in their meaning. Brand persona-descriptive words implied diverse meaning which were dependent on context.

• Journal of the Korea Fashion and Costume Design Association
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• v.10 no.1
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• pp.55-68
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• 2008

This study is aiming to set a characteristic design and a fashion trend by analyzing hat style trends and fashion images shown in fashion collections in recent seven years. Also, based on the result of the analysis on the five world's major collections, the influence and the interrelation of hat and fashion image were analyzed. The study was performed by the context analysis method and the image evaluation method. In the context analysis method, the 1,391 pictures for hat-styles which were believed to be the standard of fashion style from the S/S season of 1998 to the F/W season of 2004 were analyzed. The research is summarized as follows. Based on the result of the fashion collections, the kinds of hats came Bowler, Beret, Cloche, Capeline, Cap and Hood in order, and Casual, Feminine, Natural, Formal, Romantic, and Mannish came in order for the case of the fashion images for putting on a hat. The result of the analysis on the characteristic of fashion design according to the kinds of hats, the casual image, with highest frequency, was found from all of the kinds except Capeline. Bowler and Cloche were conspicuous in jackets/slacks, Capeline was conspicuous in one-piece shape, and cloth silhouette showed the highest frequency in H type. As for Bowler, the color of cloth and hat was mostly black, and as for Beret and Cloche, achromatic color showed the highest frequency. But as for Capeline, the cloth color, including chromatic color, was various. As for Beret, pattern and material image were various comparatively, but as for other kinds of hats, there were the materials with no pattern and with hard material image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.39-48
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• 2005

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.34-39
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• 2009

As a subjective recognition effect, human's emotion has impulsive characteristic and it expresses intentions and needs unconsciously. These are pregnant with information of the context about the ubiquitous computing environment or intelligent robot systems users. Such indicators which can aware the user's emotion are facial image, voice signal, biological signal spectrum and so on. In this paper, we generate the each result of facial and voice emotion recognition by using facial image and voice for the increasing convenience and efficiency of the emotion recognition. Also, we extract the feature which is the best fit information based on image and sound to upgrade emotion recognition rate and implement Multi-Modal Emotion recognition system based on feature fusion. Eventually, we propose the possibility of the ubiquitous computing service reasoning method based on Bayesian Network and ubiquitous context scenario in the ubiquitous computing environment by using result of emotion recognition.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.47-57
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• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• The Research Journal of the Costume Culture
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• v.8 no.4
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• pp.562-576
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• 2000

The purpose of this study is to semiotically reinterpret the Korean beauty and symbol inhering in the modern clothing of Korean designers as the context of traditional culture through regarding the clothing including hegemony that is controlling the Korean society and culture as a sign. The theoretical framework for analysis was derived from the semiotically analytic methods of F. Saussure and R. Barthes. The results of the study are as follows ; Korean images reflected in the modern clothing designed by Korean designers are humanistic image, environmental-friendly image, totemic and mythlogical image, equal and peaceful image, and metaphorical and metonymical image. Conclusively, developing a creative design based upon the interpretation of our unique and traditional culture, the clothing could be newly made of historical and cultural resources in the modern lives. If so, I think that the clothing will be not only the visual and decorative art, but also an information which contains implication of our culture, and finally can be established.

• Culinary science and hospitality research
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• v.23 no.3
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• pp.77-88
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• 2017

The purposes of this study were to verify the effects of corporate image on HMR (Home Meal Replacement) brand image, HMR product attitude and HMR behavioral intention. Using a convenience sampling method, the data were collected from those who have bought HMR in Seoul area, Korea. After a total of 350 responses were collected, 342 were used for the analyses. Both standard and hierarchical multiple regression analyses were conducted to test the hypotheses. The results are as follows. First, corporate image and HMR brand image had an effect on product attitude of HMR. Second, corporate image of HMR significantly impacted brand image of HMR. Third, brand image of HMR mediated the relationship between corporate image and behavioral intention. Fourth, product attitude of HMR had an effect on behavioral intention of HMR. These findings provide practical implication and marketing strategies for researchers and marketers regarding corporate image and brand image in the HMR context.

• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.763-771
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• 2007

As software is widely used on various environments today, there is an increasing need for adaptive software. Adaptive software is robust and flexible software that modifies its own behavior in response to the changes in its environment. Due to time constraints, high complexity and so on, it is hard to acquire context information from environment. So, when implementing software, it is common to think easily acquired data to be the environments. This research proposes an adaptive software framework that assumes plane images to be environments. Plane images are easy to acquire and have enough complexity. From this, our framework is able to acquire context information, reasons with action rule, and acts on the result of reasoning. Stand on this framework, implements software that plays a simple game automatically.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.12
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• pp.756-771
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• 2003

Digital Video Library System which manages a large amount of multimedia information requires efficient and effective retrieval methods. In this paper, we propose and implement a new video search and retrieval algorithm that compares the query video shot with the video shots in the archives in terms of foreground object, background image, audio, and its context. The foreground object is the region of the video image that has been changed in the successive frames of the shot, the background image is the remaining region of the video image, and the context is the relationship between the low-level features of the adjacent shots. Comparing these features is a result of reflecting the process of filming a moving picture, and it helps the user to submit a query focused on the desired features of the target video clips easily by adjusting their weights in the comparing process. Although the proposed search and retrieval algorithm could not totally reflect the high level semantics of the submitted query video, it tries to reflect the users' requirements as much as possible by considering the context of video clips and by adjusting its weight in the comparing process.