• Journal of the Korean Institute of Rural Architecture
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• v.13 no.4
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• pp.83-90
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• 2011

The purpose of this study is to grasp the color situation of buildings on Urban Street, through the color image evaluation to research primary factors have an effect on color image preferences. A scope of this study is district of Sang Mu in Gwangju Metropolitan City. A method of study put out evaluation model after BIB test by photography, spot color. And performed Factorial Design, Multiple Regression Analysis through SD questionnaire. The result of this study show that color situation exceedingly used YR predominates, support, stress color, in the second place used predominate, support color is B, stress color is R. Luminosity showed middle luminosity 4.1~7.0, low freshness distribution. Color image was evaluated modern and cold image and appeared affirmative aspect clean, order, neat image and showed negative aspect stiff, flat image. And 'comfortableness', 'unification' had an effect on color image preferences The result of this study showed that color plan need to improve comfortableness, unification bring control into line color at building color image of urban street.

• Fashion & Textile Research Journal
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• v.13 no.2
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• pp.162-172
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• 2011

The purpose of this study is to examine the influences of social responsibility and culture marketing on corporate image and brand equity in the casual wear market. In addition, whether corporate image and brand equity have impact on purchase intention is investigated among high school students in a local area. Two casual brands, Polham and Tate are selected for this study. The data are collected from male and female adolescents living in a local area with convenience sampling method. A total of 402 useful data are analyzed by SPSS 14.0 program. The results of this study are as follows. First, there are significant relationships among corporate social responsibility, culture marketing, corporate image, and brand equity of two brands. Second, environmental cultural support, social contribution, and economical responsibility of CSR present positive influences on corporate image and brand equity in common between two brands. Especially environmental cultural support of fashion business is highly important to improve corporate image and brand equity. Third, cultural direction and cultural business marketing are more influential than cultural sales promotion or cultural support marketing to improve corporate image and brand equity. Fourth, corporate image does not have a direct influence on the purchase intention, but brand equity factors show significant influences on the purchase intention. In conclusion, fashion companies should commit to perform corporate social responsibility and culture marketing that are suitable to target market for the long term, since these efforts would improve corporate image and build brand equity.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.303-317
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• 2002

In this paper, we design and implement E-COIRS enabling users to query with concepts and image features used for further refining the concepts. For example, E-COIRS supports the query "retrieve images containing black home appliance to north of reception set. "The query includes two types of concepts: IS-A and composite. "home appliance"is an IS-A concept, and "reception set" is a composite concept. For evaluating such a query. E-COIRS includes three important components: a visual image indexer, thesauri and a query processor. Each pair of objects in an mage captured by the visual image indexer is converted into a triple. The triple consists of the two object identifiers (oids) and their spatial relationship. All the features of an object is referenced by its old. A composite concept is detected by the triple thesaurus and IS-A concept is recolonized by the fuzzy term thesaurus. The query processor obtains an image set by matching each triple in a user with an inverted file and CS-Tree. To support efficient storage use and fast retrieval on high-dimensional feature vectors, E-COIRS uses Cell-based Signature tree(CS-Tree). E-COIRS is a more advanced content-based image retrieval system than other systems which support only concepts or image features.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1494-1496
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• 2007

This paper deals with image quality analysis considering the impact of psychological factors involved in assessment. The attributes of image quality requirement were partitioned according to the visual perception characteristics and the preference of image quality were obtained by the factor analysis method. The features of image quality which support the subjective preference were identified, The adequacy of image is evidenced to be the top requirement issues to the display image quality improvement.

• Women's Health Nursing
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• v.24 no.2
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• pp.163-173
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• 2018

Purpose: To identify factors influencing psychosocial adjustment in patients with surgical removal of benign breast tumor. Methods: With a survey design, data were collected using the Psychosocial Adjustment to Illness Scale-Self Report (PAIS-SR), Body Image Scale, Physical Discomfort Scale, and Family Support Scale with patients who had had surgical removal of a benign breast tumor from September to November 2017. Data were analysed with descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and stepwise multiple regression. Results: The mean scores for physical discomfort, body image, family support, and psychosocial adjustment were $1.57{\pm}0.51$, $0.37{\pm}0.64$, $3.62{\pm}0.67$, and $4.00{\pm}0.45$, respectively. Family support, body image, physical discomfort, number of surgical removal of benign breast tumor (twice), and cancer insurance status (yes) were verified as factors influencing psychosocial adjustment. These factors accounted for 57.4% of psychosocial adjustment. Conclusion: In this study, family support, body image, and physical discomfort were identified as significant predictors of psychosocial adjustment. Therefore, this study can be used as fundamental data to develop nursing intervention strategies in order to increase psychosocial adjustment in patients with surgical removal of a benign breast tumor.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.