• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.3
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• pp.381-386
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• 2015

In recent, many researches have been studying sensitivity and psychology of human being on color and the necessity of psychology therapy by color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously and is different from the brightness of a color. In this paper, we propose a method to extract domain colors by color classification and subdivision the classes of brightness using fuzzy inference. In addition, it is shown that our method is used for analysing the psychology status of children through their pictures.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.415-420
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• 2013

In recent, many researches have been studying sensitivity and psychology of human on color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously. In this paper, we propose the method that extract the color and direction information in order to analyze the psychology in the picture of children. Histogram clustering is used for color information detection. Direction information extract from inner edge value. In the result of experiments, we shows that our method is similar to the pattern classification of the general method.

• Smart Media Journal
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• v.7 no.4
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• pp.90-98
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• 2018

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.884-889
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• 2010

Recently, a music genre classification has been studied. However, experts use different criteria to classify each of these classifications is difficult to derive accurate results. In addition, when the emergence of a new genre of music genre is a newly re-defined. Music as a genre rather than to separate search should be classified as emotional words. In this paper, the feelings of people on the basis of brightness and darkness tries to categorize music. The proposed classification system by applying VCM(Variance Considered Machines) is the contrast of the music. In this paper, we are using three kinds of musical characteristics. Based on surveys made throughout the learning, based on musical attributes(beat, timbre, note) was used to study in the VCM. VCM is classified by the trained compared with the results of the survey were analyzed. Note extraction using the MATLAB, sampled at regular intervals to share music via the FFT frequency analysis by the sector average is defined as representing the element extracted note by quantifying the height of the entire distribution was identified. Cumulative frequency distribution in the entire frequency rage, using the difference in Timbre and were quantified. VCM applied to these three characteristics with the experimental results by comparing the survey results to see the contrast of the music with a probability of 95.4% confirmed that the two separate.

• Science of Emotion and Sensibility
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• v.5 no.2
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• pp.23-28
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• 2002

The purpose of this study was to classify apparel fabrics according to rustling rounds and to analyze their transformed colors and mechanical properties. The rustling sounds of apparel fabrics were recorded and then transformed into colors using Mori's color-transforming program. The specimens were clustered into five groups according to sound properties, and each group was named as ‘Silky’,‘Crispy’,‘Paper-like’,‘Worsted’, and ‘Flaxy’, respectively. The Silky consisted of smooth and soft silk fabrics had the lowest value of LPT, $\Delta$f, ARC , loudness(B) and sharpness(z). Their transformed colors showed lots of red portion and color counts. The Crispy with crepe fabrics showed relatively low loudness(z) and sharpness(B), but diverse colors and color counts were appeared. The Paper-like showed the highest value of LPT, $\Delta$f and loudness(z). The Worsted composed of wool and wool-Like fabrics showed high values of LPT, $\Delta$f, loudness(z) and sharpness(B). The transformed rotors of the Paper-like and Worsted showed the blue mostly but color counts were less than the others. The Flaxy with rugged flax fabric had the highest fluctuation strength, and their transformed colors showed diversity.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.351-362
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• 2013

This study aims to investigate the characteristics of Chinese consumers, who are growing up as the global biggest consumption market, according to G sensibility types, and to provide the data base for China market by analyzing the consuming pattern per sensibility and the preferred color. For the investigation, the survey on G sensibility and consuming pattern was conducted for consumers in four cities of China. As a result of classification of G sensibility types, it was found that Chinese consumers tended to behave in accordance with their values and identities and the most general type in them was G1 pursuing the reasonable and logical consumption, unlike Korean consumers who tended to be shown as G3 for the most general type according to the preceding study. As to characteristic of consumption, Chinese consumers preferred to purchase clothes from the department store, and in case of G2 type, the characteristics was corresponded with Actionist's character which shows the wide range of behavior and high-consumption, by preferring the road-shop next to the department store. Chinese consumers tended to purchase the clothes on the basis of their preferred colors, and especially it was shown that achromatic color was very commonly preferred. Also the black color was on the highest preference, and white, dark gray and light gray were followed. Meanwhile, in chromatic color, it was found that brown, orange, red and blue were preferred in order, and in case of G4, it was found that they preferred more various colors compared to the other types. This result could be used as the data base for the marketing strategy of fashion design industry and the related companies, as well as the new communication method for the consumers.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.83-100
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• 2019

The purposes of this study are to identify the meaning of the geometrical patterns preferred by ancient Korean peoples and to classify them into some groups by their similarity. We investigated various patterns found on clothing and relics from GoJoseon to Goguryeo period, and utilized secondary sources such as history articles, Internet materials and photo and analyzed the associations of the varied patterns found in pottery, handicrafts, and clothing with the ancient cultures. We found the letters (ㅇ, ㅁ, and ㅅ of Korean alphabet, Hangul) preferred by ancestors who worshipped nature to identify the significations attached by them to particular patterns. The results confirm the following: first, the circle pattern indicated the sun, moon, stars in the sky, a bronze mirror, and a man's face. Circles and ovals were also observed to represent the individual souls of the clan or community. Second, square patterns symbolized the land and the patterns that signified the wellbeing of family and the country. Oblique rectangles were more frequently used as they represented a double use of the triangle, a shape that implied mystic power. Third, triangle symbolized regeneration, power, and humanity. While the Neolithic Age jade remnants of hair combs appear not to be irrelevant to the process of comb-shaped pottery production of the time, many fine comb-like lines may be found on bronze mirrors. Through its review of the glorious designs inherited from and established by ancient ancestors, the present research endeavor may help in identifying the spirits and traditions of Korean history.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Smart Media Journal
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• v.7 no.3
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• pp.43-50
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• 2018

Hanok (a traditional Korean-style house) has recently become a popular attraction for tourists all over the world. Jeonju Hanok Village, for example, attracted about 10 million visitors for 2 consecutive years. Observing Hanok's popularity, many local governments drew up plans to improve tourism dynamics by strengthening the advantages of Hanok. Emotionally rich experience is required to offer a greater satisfying experience that meets the demands of tourists. However, very few studies yet have addressed how to measure those emotions felt by users while experiencing Hanok. As an attempt to improve this situation, 182 emotional words were collected from earlier studies and classified into 33 groups with the Delphi method. Among the emotional words in each of the 33 groups, those of overlapping concepts on the characteristics of Hanok were re-grouped, and extracted the most appropriate 68 words. Additionally, a survey was conducted with 325 people who had experienced Hanok to gather 30-most representative emotions for measuring emotions felt from Hanok. The factor analysis of the 30 representative emotions resulted in classified 6 factors based on common features of emotional words: senses of aesthetics, happiness, novelty, ownership, balance and relaxation. The 30 representative emotions and six emotion categories found out by this study can help measure how much people feel certain emotions while experiencing hanoks. Further study will explore the degree of emotions hanok users feel about objects of hanok, such as roof materials and shapes, and body shapes.