• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.20-27
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• 2003

In this paper, we implemented a content-based image retrieval system that user can choose a wanted query region of object and retrieve similar object from image database. Query image is induced to wavelet transformation after divided into hue components and gray components that hue features is extracted through color autocorrelogram and dispersion in hue components. Texture feature is extracted through autocorrelogram and GLCM in gray components also. Using features of two components, retrieval is processed to compare each similarity with database image. In here, weight value is applied to each similarity value. We make up for each defect by deriving features from two components beside one that elevations of recall and precision are verified in experiment results. Moreover, retrieval efficiency is improved by weight value. And various features of database images are indexed automatically in feature library that make possible to rapid image retrieval.

• Journal of the Korean Society of Costume
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• v.43
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• pp.187-202
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• 1999

The objectives of this study were to analyze the effect of dress(Korean traditional dress and suit) of elderly Women and situation on impression formation. The experimental design was $10\times{2}\times{2(dress}\times{perceiver's age}\times{situation)}$ factorial design by 3 independent variables. The stimuli of color photographs of female in her 60's model and the semantic differential scale were used. Six variables of impression formation were used: preference: elegance: potency: activity: feminine: and modernity. Samples were 400 women 200 were in their twenties and 200 in their forties and fifties. The data were analyzed by $\alpha$-reliability t-test ANOVA and duncan's multiple range test. The Korean traditional dress with the combination of Korean traditional color(light blue upper dress with dark red purple collar and string.dark blue skit) had the most positive effect on impression of elegance. Pink traditional dress and light blue traditional dress had a negative effect on impression of potency activity and modernity. Red purple suit had a positive effect on potency and modernity. The interaction between dress perceiver's age and stituation was significant for the impression of activity. Women in their 40's and 50's perceived the activity of red purple suit positively in the situation of alumnae meeting more than in the wedding ceremony. The perceived age of the stimulus person was different according to dresses. Traditional dresses was perceived older than suits were. Women in their 40's and 50's evaluated preferences of the dresses positively more than 20's did. This means that 40's and 50's feel similarity with the stimulus person more than 20's as the age of model was in their 60's The result supports the theory that similarity is basic factor in interpersonal attraction.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Cartoon and Animation Studies
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• s.40
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• pp.477-496
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• 2015

Emotional awareness is the image of a person is represented by different tendencies. Currently, the emotion computing to objectively evaluate the emotion recognition research is being actively studied. However, existing emotional computing research has many problems to run. First, the non-objective in emotion recognition if it is inaccurate. Second, the correlation between the emotion recognition is unclear points. So to test the regularity of image sensitivity to the need of the present study is to control emotions in the computing system. In addition, the screen number of the emotion recognized for the purpose of this study, applying the method of objective image emotional computing system and compared with a similar degree of emotion of the person. The key features of the image emotional computing system calculates the emotion recognized as numbered digital form. And to study the background of emotion computing is a key advantage of the effect of the James A. Russell for digitization of emotion (Core Affect). Pleasure emotions about the core axis (X axis) of pleasure and displeasure, tension (Y-axis) axis of tension and relaxation of emotion, emotion is applied to the computing research. Emotional axis with associated representative sensibility very happy, excited, elated, happy, contentment, calm, relaxing, quiet, tired, helpless, depressed, sad, angry, stress, anxiety, pieces 16 of tense emotional separated by a sensibility ComputingIt applies. Course of the present study is to use the color of the color key elements of the image computing formula sensitivity, brightness, and saturation applied to the sensitivity property elements. Property and calculating the rate sensitivity factors are applied to the importance weight, measured by free-level sensitivity score (X-axis) and the tension (Y-axis). Emotion won again expanded on the basis of emotion crossed point, and included a representative selection in Sensibility size of the top five ranking representative of the main emotion. In addition, measuring the emotional image of a person with 16 representative emotional score, and separated by a representative of the top five senses. Compare the main representative of the main representatives of Emotion and Sensibility people aware of the sensitivity of the results to verify the similarity degree computing emotion emotional emotions depending on the number of representative matches. The emotional similarity computing results represent the average concordance rate of major sensitivity was 51%, representing 2.5 sensibilities were consistent with the person's emotion recognition. Similar measures were the degree of emotion computing calculation and emotion recognition in this study who were given the objective criteria of the sensitivity calculation. Future research will need to be maintained weight room and the study of the emotional equation of a higher concordance rate improved.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.48-55
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• 2008

In this paper we propose a novel method for image retrieval system using image segmentation and salient points. The proposed method consists of four steps. In the first step, images are segmented into several regions by JSEG algorithm. In the second step, for the segmented regions, dominant colors and the corresponding color histogram are constructed. By using dominant colors and color histogram, we identify candidate regions where objects may exist. In the third step, real object regions are detected from candidate regions by SIFT matching. In the final step, we measure the similarity between the query image and DB image by using the color correlogram technique. Color correlogram is computed in the query image and object region of DB image. By experimental results, it has been shown that the proposed method detects multi-object very well and it provides better retrieval performance compared with object-based retrieval systems.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.289-296
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• 1999

In this paper, an improved fractal color image decoding method using the data dependence parts and the vector distortion measure is proposed. The vector distortion measure exploits the correlation between different color components. The pixel in RGB color space can be considered as a 30dimensional vector with elements of RGB components. The root mean square error(rms) in RGB color for similarity measure of two blocks R and R' was used. We assume that various parameter necessary in image decoding are stored in the transform table. If the parameter is referenced in decoding image, then decoding is performed by the recursive decoding method. If the parameter is not referenced in decoding image, then the parameters recognize as the data dependence parts and store its in the memory. Non-referenced parts can be decoded only one time, because its domain informations exist in the decoded parts by the recursive decoding method. Non-referenced parts are defined the data dependence parts. Image decoding method using data dependence classifies referenced parts and non-referenced parts using information of transform table. And the proposed method can be decoded only one time for R region decoding speed than Zhang & Po's method, since it is decreased the computational numbers by execution iterated contractive transformations for the referenced range only.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.1-11
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• 2007

As the availability of an image information has been significantly increasing, necessity of system that can manage an image information is increasing. Accordingly, we proposed the region-based content retrieval(CBIR) algorithm based on an efficient combination of an image segmentation, an image texture, a color feature and an image's shape and position information. As a color feature, a HSI color histogram is chosen which is known to measure spatial of colors well. We used active contour and CWT(complex wavelet transform) to perform an image segmentation and extracting an image texture. And shape and position information are obtained using Hu invariant moments in the luminance of HSI model. For efficient similarity computation, the extracted features(color histogram, Hu invariant moments, and complex wavelet transform) are combined and then precision and recall are measured. As a experimental result using DB that was supported by www.freefoto.com. the proposed image retrieval engine have 94.8% precision, 82.7% recall and can apply successfully image retrieval system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.216-222
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• 2008

This paper proposed the multi characters detection algorithm using Color segmentation and the closing curve feature of LoG Operator in order to complement the demerit of the existing research which is weak in complexity of background, variety of light and disordered line and similarity of left and background color, etc. The proposed multi characters detection algorithm divided into three parts : The feature detection, characters format and characters detection Parts in order to be possible to apply to image of various feature. After preprocess that the new multi characters detection algorithm that proposed in this paper used wavelet, morphology, hough transform which is the synthesis logical model in order to raise detection rate by acquiring the non-perfection characters as well as the perfection characters with processing OR operation after processing each color area by AND operation sequentially. And the proposal algorithm is simulated with natural images which include natural character area regardless of size, resolution and slant and so on of image. And the proposal algorithm in this paper is confirmed to an excellent detection rate by compared with the conventional detection algorithm in same image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.9-17
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• 2007

In this study, Digital Elevation Models (DEMs) were constructed from color images, grayscale images and each bands (Red, Green, Blue) of color image, and the accuracies of each DEMs were evaluated, And then, correlation coefficients between left and right images of each stereopairs were analyzed. The DEM can be constructed conventionally from the digital map and stereopair images using image matching. The image matching requires stereo satellite images or aerial photographs. In case of rotor aerial photographs, these are to be scanned in 3 bands (Red, Green, Blue). For this study, 5 types of images were acquired; color, grayscale, RED band, GREEN band, and BLUE band image. DEMs were constructed from 5 types of stereopair images and evaluated using elevation points of digital maps. In order to analyze the cause of various accuracies of each DEMs, the similarity between left and right images of each stereopairs were analyzed. Consequently, the accuracy of the DEM constructed from RED band images of color aerial photograph were proved best.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.13-21
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• 2019

The content based on image retrieval makes use of features of information within image such as color, texture and share for Retrieval data. we present a novel approach for improving retrieval accuracy based on DCT Filter-Bank. First, we perform DCT on a given image, and generate a Filter-Bank using the DCT coefficients for each color channel. In this step, DC and the limited number of AC coefficients are used. Next, a feature vector is obtained from the histogram of the quantized DC coefficients. Then, AC coefficients in the Filter-Bank are separated into three main groups indicating horizontal, vertical, and diagonal edge directions, respectively, according to their spatial-frequency properties. Each directional group creates its histogram after employing Otsu binarization technique. Finally, we project each histogram on the horizontal and vertical axes, and generate a feature vector for each group. The computed DC and AC feature vectors bins are concatenated, and it is used in the similarity checking procedure. We experimented using 1,000 databases, and as a result, this approach outperformed the old retrieval method which used color information.