• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1795-1798
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• 2003

내용을 기반으로 하는 영상검색에 있어 색상과 물체의 특징은 중요한 요소로서, 지금까지의 검색 기법들은 이들을 중심으로 연구가 진행되어 왔으며, 이들을 추출하기 위해서는 color 영상에서의 배경과 물체의 분리는 선행되어야 할 중요한 과제이다. color 영상에서 물체를 분리 하고자 하는 여러 가지 시도가 있었으나, 대부분 clustering 에 준하고 있으며, 처리시간이나 결과에 있어서 그다지 좋은 효과를 내지 못하는 것도 사실이다. 따라서, 영상검색을 위한 물체의 분리 기법으로서는 적합하지 않다. 본 논문에서는 물체가 영상의 중심에 주로 위치한다는 점에 착안한 방법을 응용하여 영상의 외곽에 존재하는 색상뿐만 아니라 명암까지 분석하여, 배경을 구성하는 화소들의 색상 및 명암과 동일하지 않은 색상들로 이루어진 부분을 물체로 판단, 추출하는 기법에 대해 설명하고, edge를 추출해낸 영상의 정보와 합성하여 최적의 물체를 찾아 검색을 하는 기법에 대하여 기술하였다.

• Smart Media Journal
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• v.2 no.1
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• pp.31-36
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• 2013

A robust and efficient text extraction is very important for an accuracy of Optical Character Recognition (OCR) systems. Natural scene images with degradations such as uneven illumination, perspective distortion, complex background and multi color text give many challenges to computer vision task, especially in text extraction. In this paper, we propose a method for extraction of the text in signboard images based on a combination of mean shift algorithm and weighting scheme of hue and saturation in HSI color space for clustering algorithm. The number of clusters is determined automatically by mean shift-based density estimation, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. Weighting scheme of hue and saturation is used for formulation a new distance measure in cylindrical coordinate for text extraction. The obtained experimental results through various natural scene images are presented to demonstrate the effectiveness of our approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.868-871
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• 2009

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.39-50
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• 2024

Color is an essential visual element that has a significant impact on the formation of a city's image and people's perceptions. Quantitative analysis of color in urban environments is a complex process that has been difficult to implement in the past. However, with recent rapid advances in Machine Learning, it has become possible to analyze city colors using photos shared by tourists. This study selected Dali City, a popular tourist destination in China, as a case study. Photos of Dali City shared by tourists were collected, and a method to measure large-scale city colors was explored by combining machine learning techniques. Specifically, the DeepLabv3+ model was first applied to perform a semantic segmentation of tourist sharing photos based on the ADE20k dataset, thereby separating artificial elements in the photos. Next, the K-means clustering algorithm was used to extract colors from the artificial elements in Dali City, and an adjacency matrix was constructed to analyze the correlations between the dominant colors. The research results indicate that the main color of the artificial elements in Dali City has the highest percentage of orange-grey. Furthermore, gray tones are often used in combination with other colors. The results indicated that local ethnic and Buddhist cultures influence the color characteristics of artificial elements in Dali City. This research provides a new method of color analysis, and the results not only help Dali City to shape an urban color image that meets the expectations of tourists but also provide reference materials for future urban color planning in Dali City.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.10-19
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• 2003

Caption information in news videos can be useful for video indexing and retrieval since it usually suggests or implies the contents of the video very well. In this paper, a new algorithm for extracting and recognizing characters from news video is proposed, without a priori knowledge such as font type, color, size of character. In the process of text region extraction, in order to improve the recognition rate for videos with complex background at low resolution, continuous frames with identical text regions are automatically detected to compose an average frame. The image of the averaged frame is projected to horizontal and vertical direction, and we apply region filling to remove backgrounds to produce the character. Then, K-means color clustering is applied to remove remaining backgrounds to produce the final text image. In the process of character recognition, simple features such as white run and zero-one transition from the center, are extracted from unknown characters. These feature are compared with the pre-composed character feature set to recognize the characters. Experimental results tested on various news videos show that the proposed method is superior in terms of caption extraction ability and character recognition rate.

• Journal of Embryo Transfer
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• v.30 no.2
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• pp.99-107
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• 2015

The aim of the present study was to identify coat color associated genes that are differentially expressed in mature Korean brindle cattle (KBC) with different coat colors and in Hanwoo cows. KBC calves, before and after coat color appearance, were included. Total cellular RNA was isolated from the tail hair cells and used for microarray. The number of expressed coat color associated genes/probes was 5813 in mature KBC and Hanwoo cows. Among the expressed coat color associated genes/probes, 167 genes were the coat color associated genes listed in the Gene card database and 125 genes were the pigment and melanocyte genes listed in the Gene ontology_bovine database. There were 23 genes/probes commonly listed in both databases and their expressions were further studied. Out of the 23 genes/probes, MLPH, PMEL, TYR and TYRP1 genes were expressed at least two fold higher (p<0.01) levels in KBC with brindle color than either Hanwoo or KBC with brown color. TYRP1 expression was 22.96 or 19.89 fold higher (p<0.01) in KBC with brindle color than either Hanwoo or KBC with brown color, respectively, which was the biggest fold difference. The hierarchical clustering analysis indicated that MLPH, PMEL, TYR and TYRP1 were the highly expressed genes in mature cattle. There were only a few genes differentially expressed after coat color appearance in KBC calves. Studies on the regulation and mechanism of gene expression of highly expressed genes would be next steps to better understand coat color determination and to improve brindle coat color appearance in KBC.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.11
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• pp.1338-1349
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• 2003

The color of apparels has the close interdependency on the skin colors of the wearers. This study was carried out to group the skin colors of Korean males into several similar skin colors and to analyze their preference colors. The skin colors were measured quantitatively and classified into several clusters that has similar hue, value and chroma with Munsell color system that is internationally used to communicate the colors. Sample size was 420 Korean males. With color spectrometer, JX-777, 4 points of the body were measured. All subjects had been shown with 40 color chips and answered their preference colors. Data were analysed by K-means Cluster analysis, Duncan test, Frequency and Chi square test using SPSS WIN 10 statistical package. Findings were as follows: 1. The skin colors of Korean males were mixed with skin colors of YR, R, and Y. 2. 420 subjects who have YR color were clustered in 3 kinds of skin color groups. 3. The average face color of total subjects was 4.81YR 5.91/4.97 in Munsell color system, 60.74 in L value, 13.71 in a value, 24.54 in b value. 136 observations out of 420 subjects were composed of Type 1: 4.50YR 6.35/4.87 and 192 observations were composed of Type 2: 4.62YR 5.86/5.12 and 92 observations were composed of Type 3: 5.67YR 5.37/4.79. 4. The average skin color of total 420 subjects was 6.26YR 6.07/4.41 and 62.33 in L value, 10.64 in a value, 23.48 in b value. The average skin color of Type 1 was 6.27YR 6.44/4.27 and of Type 2 was 6.15YR 5.91/4.49 and of Type 3 was 6.49YR 5.84/4.43 respectively. 5. 3 groups showed that the most preference color of sport$.$casual was 2.5Y 8/16 and 7.5PB 4/16 and the most preference color to their skins was 7.5PB 4/16 and 7.5YR 7/16.

• International Journal of Contents
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• v.8 no.4
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• pp.95-102
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• 2012

In this paper, we present a technique to extract the title areas from book cover images. A typical book cover image may contain text, pictures, diagrams as well as complex and irregular background. In addition, the high variability of character features such as thickness, font, position, background and tilt of the text also makes the text extraction task more complicated. Therefore, we propose a two steps efficient method that uses Histogram of Oriented Gradients and color information to find the title areas. Firstly, text localization is carried out to find the title candidates. Finally, refinement process is performed to find the sufficient components of title areas. To obtain the best result, we also use other constraints about the size, ratio between the length and width of the title. We achieve encouraging results of extracted title regions from book cover images which prove the advantages and efficiency of the proposed method.