• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.253-260
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• 1999

In this paper, we suggest a new direct method for mage segmentation using texture and color information combined through a multivariate linear discriminant algorithm. The color texture is computed in nin 3${\times}$3 masks obtained from each 3${\times}$3${\times}$3 spatio-spectral neighborhood in the image using the classical haralick and Pressman texture features. Among these 9${\times}$28 texture features the best set was extracted from a training set. The resulting set of 10 features were used to segment an image into four different regions. The resulting segmentation was Compared to classical color and texture segmentation methods using both box classifiers and maximum likelihood classification. It compared favourably on the test image from a Fastred-Lightgreen stained prostatic histological tissue section based on visual inspection. The classification accuracy of 97.5% for the new method obtained on the training data was also among the best of the tested methods. If these results hold for a larger set of images, this method should be a useful tool for segmenting images where both color and texture are relevant for the segmentation process.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.483-488
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• 2015

This paper presents a local binary pattern(LBP) for effectively classifying textures, which is based on the multidimensional intensity difference between the adjacent pixels in the block image. The intensity difference by considering the a extent of 4 directional changes(verticality, horizontality, diagonality, inverse diagonality) in brightness between the adjacent pixels is applied to reduce the computation load as a results of decreasing the levels of histogram for classifying textures of image. And the binary patterns that is represented by the relevant intensities within a block image, is also used to effectively classify the textures by accurately reflecting the local attributes. The proposed method has been applied to classify 24 block images from USC Texture Mosaic #2 of 128*128 pixels gray image. The block images are different in size and texture. The experimental results show that the proposed method has a speedy classification and makes a free size block images classify possible. In particular, the proposed method gives better results than the conventional LBP by increasing the range of histogram level reduction as the block size becomes larger.

• The Research Journal of the Costume Culture
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• v.16 no.2
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• pp.293-304
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• 2008

The objectives of this study were to investigate the images in men's hairstyle by hair color, tone, texture, and perceiver's gender, and to examine the characteristics of hairstyle appropriate to seasons. A quasi-experimental method by questionnaire was used, and the experimental design was $4{\times}3{\times}2{\times}2$(hair color$\times$tone$\times$texture$\times$perceiver's$\times$gender) factorial design. The subjects were 372 men and women in their 20s through 50s. five factors of men's hairstyle image were derived by factor analysis: individuality, dignity, romanticism, refinement, and activity. Black hair was perceived to be high in dignity and activity. Bright tone was perceived to be high in individuality, but low in dignity. Men's wave hair was perceived to be higher in individuality than straight hair, but lower in dignity. Perceiver's gender did not give significant influence on evaluation of all image factors. In brown, neutral tone was perceived to be higher in dignity. romanticism, and activity than dark or bright tone. In black, wave hair was perceived to be more refined than straight hair. Black hair matches with winter the most, and yellow matches with spring the most. In terms of tone, dark tone matches with winter; neutral tone matches with autumn; bright tone matches with summer. The results of this study verified that hair color and texture affect men's image perception, and matching hair colors are associated with seasons.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.183-194
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• 1999

The Brightness, color and texture included in a multispectral satellite data are used as important factors to analyze and to apply the image data for a proper use. One of the most significant process in the satellite data analysis using texture or color information is to extract features effectively expressing the information of original image. It was described in this paper that six features were introduced to extract useful features from the analysis of the satellite data, and also a classification network using the back-propagation neural network was constructed to evaluate the classification ability of each vector feature in SPOT imagery. The vector features were adopted from the training set selection for the interesting region, and applied to the classification process. The classification results showed that each vector feature contained many merits and demerits depending on each vector's characteristics, and each vector had compatible classification ability. Therefore, it is expected that the color and texture features are effectively used not only in the classification process of satellite imagery, but in various image classification and application fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.201-205
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• 2020

The structural texture is defined as a form which a texel is regularly repeated in the texture. Structural texture analysis/recognition has various industrial applications, such as automatic inspection of textiles, automatic testing of metal surfaces, and automatic analysis of micro images. In this paper, we propose a Convolution Neural Network (CNN) based system for structural texture analysis. The proposed method learns texles, which are components of textures to be classified. Then, this trained CNN recognizes a structural texture using a partial image obtained from input texture. The experiment shows the superiority of the proposed system.

• Journal of the Korean Society of Costume
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• v.18
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• pp.269-281
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• 1992

The purposes of this study were to 1) classify the contents of clothing image preferences, 2) find out the relationship among personal values, preferences for clothing image and clothing design, and 3) investigate the relationship between clothing image preferences and clothing design preferences, Questionnaire was comprised of three section. The clothing image preference measure was included 36 bipolar adjectives of 7-point scales. Clothing design preferences measure was included the items of patterns, colors, and textures. 'Survey of Personal Values' by Eung-Un Hwang and Kyung -hye Lee was used for measurement of 6 values : practical mindedness ; achievement ; variety ; decisiveness; orderliness; and goal orientation. Samples were 288 college women. The data were analyzed using pearson's correlation coefficient and factor analysis. The results of the study were the following. 1. Four segments of clothing image preferences derived by factor analysis : F. 1 'progressive-conservative' ; F.2. 'casual-formal'; F.3 'plain-splendid'; F.4 'masculine-feminine'. 2. In relation between personal values and clothing image preferences, 1) achievement was positively related to the preference of progressive image 2) variety was positively related to the preferences of progressive and masculine image, and 3) goal orientation was negatively related to the preferences of the progressive and masculine image, and positively related to plain image. 3. In relation between personal values and clothing design preferences, 1) practical mindedness was positively related to the preference of black, 2) achievement was positively related to the preferences of blue and such realistic pattern as floral, 3) variety was positively related to the preferences of geometric or abstract patterns and thick or transparent texture, and 4) orderliness was negatively related to the preferences of abstract pattern. 4. In relation between clothing image preferences and clothing design preferences, 1) progressive image was positively related to abstract pattern, red, blue, and black, 2) casual image was positively related to geometric pattern, green, blue, and negatively related to red and soft rexture, 3) plain image was negatively related to lustered and transparent texture, abstract pattern, red, and black, and 4) masculine image was negatively related to lustered, thin, soft, and transparent texture, floral and dotted patterns, red, orange, and yellow.

• Computers and Concrete
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• v.14 no.3
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• pp.315-325
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• 2014

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

• Journal of Biosystems Engineering
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• v.22 no.4
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• pp.433-438
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• 1997

The freshness of apple was evaluated by characterizing the surface texture of flesh cells. The freshness index which was related to the amount of wrinkles on the cell surface was defined to quantify the freshness. Four parameters relevant to the amount of the cell wrinkles were selected and measured using image analysis including wrinkle extraction procedure and Fast Fourier Transform of a wrinkle-extracted image. Out of 4 parameters, three parameters had highly significant correlations with the time elapsed after harvest. But it was concluded that two parameters out of such 3 parameters could be used for description of freshness index.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.387-388
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• 2007

In this paper, a content-based image retrieval method considering both local information and spatial correlation of image is proposed. In order to efficiently represent the spatial correlation, texture structure is classified into three kinds of pattern. In experiment result, our method improves $3.94%{\sim}11.23%$ precision rate over the existing methods.