• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.10
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• pp.1-13
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• 2011

The aim of this study is to analyze the emotion adjectives for light emitting diode(LED) light colors using a twofold adjective image scales from Kobayashi and I.R.I. A set of psychophysical experiments using category judgment was conducted in an LED light color simulation system, in order to evaluate each emotion scale coordinate for those test light colors in both adjective image scales. In total, 49 test light colors from a combination of 6 color series were assessed by 15 human observers. As a result, Kobayashi adjective image scale clearly expressed to emotion adjectives of 'Dynamic', 'Casual', 'Chic', 'Cool-casual', 'Modern', and 'Natural' for different hues. In contrast, I.R.I adjective image scale expressed only 2 adjectives of 'dynamic' and 'luxurious' for the all hues.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.289-297
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• 2007

Color image scale captures the knowledge of colorists and represents both adjectives and colors in the same adjective image scales in order to select color(s) corresponding to an adjective. Due to the difficulty of psychological experiment and statistical analysis, in general, only a limited number of colors are located in the color image scales. This can make color selection process hard especially to non-expert. In this paper, we propose an interpolation of color image scale based on the fuzzy K-nearest neighbor method, which provides continuous colors according to the coordinates of the image scales. The experimental results show that the interpolated image scales can be practically useful for color selection process.

• Proceedings of the Korea Society of Design Studies Conference
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• 1998.05a
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• pp.17-18
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• 1998

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.99-107
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• 2019

Retinex-based image enhancement is a technique that utilizes the property that the human visual characteristics are sensitive to the difference from the surrounding pixel value rather than the pixel value itself. These Retinex-based algorithms show different characteristics of the improved image depending on the applied color space or gamma correction. In this paper, we set eight different experimental conditions according to the application of color space and gamma correction, and analyze the objective and subjective performance of each Retinex based image enhancement algorithm and apply it to the implementation of Retinex based algorithm. In the case of gamma correction, quantitative low entropy images and low contrast images are obtained. The application of Retinex technique in HSI color space rather than RGB color space is found to be high in overall subjective image quality as well as maintaining color.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1481-1490
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• 2017

This paper presents an image quality enhancement algorithm for dark image acquired under poor lighting condition. Various retinex algorithms which are human perception-based image processing methods were proposed to solve this problem. Although MSR(Multi-Scale Retinex) among these algorithm works well under most lighting condition, it shows color degradation because their separate nonlinear processing of RGB color channels. To compensate for the loss of the color, MSRCR(Multi-Scale Retinex with Color Restoration) was proposed. However, it requires high computational load and has additional parameters that need to be adjusted according to input image. In order to overcome this problem, a new retinex algorithm based on MSR is proposed in this paper. The proposed method consists of V channel MSR, saturation correction, and separate contrast enhancement process. Experimental results show that the subjective and objective image quality of the proposed method better than those of the conventional methods.

• Journal of Korean Society of Rural Planning
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• v.16 no.3
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• pp.1-10
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• 2010

The purpose of this study is to find out the color preference on rural rooftop color. Today, public profit value of a rural community has been heavily recognized in the areas of preservation of pro-environmental and traditional life styles and foundation of collective community. Comparatively with urban, rural town conserves the scenic of those days when the historic landscape existed. Therefore, elements of historic and tradition in rural town are interesting resources to people who want traditional experience. Color is one of the most influenced elements for the image of Landscape. On the other hand, radical modernization has brought conflicting color element that does not blend with existing environmental color. Among others, roof represents one of the important traits of rapidly developing rural community due to vast amount of space it covers. In order to come up with more appropriate coloring scheme, it is necessary determine color that forms a balance with present conditions of rural life and historic landscape. This study provides more objective rooftop coloring selection process by employing additional surveys regarding color image adjective. I justified necessity of the balance between environmental color and roof color through analyzing correlation between environmental color of the rural town and Color Image Scale of the preliminary selection of roofs.

• Journal of the Korean Society of Costume
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• v.62 no.6
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• pp.38-52
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• 2012

The purpose of this study is to confirm the international unique color sensibility according to the ethnic color preferences. The existing studies about color sensibility were investigated to analyze the international color sensibility. The countries were chosen according to its, strong regional and racial color. Also, the documents and websites about environment color such as structure color, natural feature color, traditional folk costume color and customary color names were investigated, and then, the international color sensibility was analyzed by using the color image scale. As a result of the analysis about the differences of color sensibility, internationally distinguished color sensibility was discovered. There were differences not only for the preference trend of hue but also for the tone or contrast of color among the selected countries. Especially, Great Britain had a strong preference for G categories that they preferred the warm-grayish color image. Russia has a preference for R, G, and B categories with the preference for the warm-clear image. Netherlands had a preference for R, Y, and PB categories and it preferred the cool-hard-grayish, warm-soft-clear image. Italy had a preference for R and Y categories and it preferred the warm-clear image. Morocco had a preference for R and B categories and it preferred the warm and cool, clear image. Japan had a preference for R, G categories and it preferred the warm-grayish image. Korea had a preference for R and B categories and it preferred the warm-soft-clear, and cool-clear image. With these results, the researcher concludes that the integrated analysis of the environment color and the traditional racial color factors are very persuasive methods to comprehend the international color sensibility.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.4
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• pp.1-8
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• 2023

This study is meaningful in figuring out how much the Korean people's awareness of hanok has increased even though interest in hanok has also increased due to the Korean Wave craze. Therefore, with respect to the exterior of hanok, which is visually recognized first, the level of experts and ordinary people is grasped through a semantic discrimination scale, and the degree of visual recognition is to be investigated centering on the color image of hanok buildings. This is the process of thinking about how the Korean image should be reflected in the design, and we want to suggest the direction that modern hanok should go. The study compared and analyzed the difference in visual color based on the elevation of the hanok using a 7-point and 5-point scale method for the general public and experts, and utilized the IRI adjective vocabulary scale and the color matching image scale to construct new hanoks with insufficient differences in appearance and shape. It can be applied to design and image preservation and construction of existing hanok.