• Title/Summary/Keyword: color feature space

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Face Feature Extraction Method ThroughStereo Image's Matching Value (스테레오 영상의 정합값을 통한 얼굴특징 추출 방법)

  • Kim, Sang-Myung;Park, Chang-Han;Namkung, Jae-Chan
    • Journal of Korea Multimedia Society
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    • v.8 no.4
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    • pp.461-472
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    • 2005
  • In this paper, we propose face feature extraction algorithm through stereo image's matching value. The proposed algorithm detected face region by change the RGB color space of skin color information to the YCbCr color space. Applying eye-template from extracted face region geometrical feature vector of feature about distance and lean, nose and mouth between eye extracted. And, Proposed method could do feature of eyes, nose and mouth through stereo image's matching as well as 2D feature information extract. In the experiment, the proposed algorithm shows the consistency rate of 73% in distance within about 1m and the consistency rate of 52%in distance since about 1m.

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Image Retrieval using Fast Wavelet Histogram and Color Information (고속 웨이블렛 히스토그램과 색상정보를 이용한 영상검색)

  • 김주현;이배호
    • Proceedings of the IEEK Conference
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    • 2000.06d
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    • pp.194-197
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    • 2000
  • Wavelet transform used for content-based image retrieval has good performance in texture image. Image features for content-based image retrieval are color, texture, and shape. In this paper, we use color feature extracted from HSI color space known as most similar vision system to human vision system and texture feature extracted from wavelet histogram which has multiresolution property. Proposed method is compared with HSI color histogram method and wavelet histogram method. It is shown better performance.

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Color Space Based Objects Detection System from Video Sequences

  • Alom, Md. Zahangir;Lee, Hyo Jong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2011.11a
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    • pp.347-350
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    • 2011
  • This paper propose a statistical color model of background extraction base on Hue-Saturation-Value(HSV) color space, instead of the traditional RGB space, and shows that it provides a better use of the color information. HSV color space corresponds closely to the human perception of color and it has revealed more accuracy to distinguish shadows [3] [4]. The key feature of this segmentation method is based on processing hue component of color in HSV color space on image area. The HSV color model is used, its color components are efficiently analyzed and treated separately so that the proposed algorithm can adapt to different environmental illumination condition and shadows. Polar and linear statistical operations are used to calculate the background from the video frames. The experimental results show that the proposed background subtraction method can automatically segment video objects robustly and accurately in various illuminating and shadow environments.

Comparison of Feature Selection Processes for Image Retrieval Applications

  • Choi, Young-Mee;Choo, Moon-Won
    • Journal of Korea Multimedia Society
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    • v.14 no.12
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    • pp.1544-1548
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    • 2011
  • A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

A Comparison of Superpixel Characteristics based on SLIC(Simple Linear Iterative Clustering) for Color Feature Spaces (칼라특징공간별 SLIC기반 슈퍼픽셀의 특성비교)

  • Lee, Jeong Hwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.10 no.4
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    • pp.151-160
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    • 2014
  • In this paper, a comparison of superpixel characteristics based on SLIC(simple linear iterative clustering) for several color feature spaces is presented. Computer vision applications have come to rely increasingly on superpixels in recent years. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. A superpixel is consist of pixels with similar features such as luminance, color, textures etc. Thus superpixels are more efficient than pixels in case of large scale image processing. Generally superpixel characteristics are described by uniformity, boundary precision and recall, compactness. However previous methods only generate superpixels a special color space but lack researches on superpixel characteristics. Therefore we present superpixel characteristics based on SLIC as known popular. In this paper, Lab, Luv, LCH, HSV, YIQ and RGB color feature spaces are used. Uniformity, compactness, boundary precision and recall are measured for comparing characteristics of superpixel. For computer simulation, Berkeley image database(BSD300) is used and Lab color space is superior to the others by the experimental results.

Content based image retrieval using maximum color

  • Park, Jong-An
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.6 no.4
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    • pp.232-237
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    • 2013
  • This paper presents image database retrieval based on maximum color occurrenceusing Hue, Saturation and Value (HSV) color space. Our system is based on color segmentation. We dividedthe image into n number of areas based on different selected ranges of hue and value, then each area is partitioned into m number of segments based on the number of pixels it contains, after this we calculated the maximumcolor occurrence in each segment and used its HSV value. This is used as a feature vector.

A Palette of Color Combination Based on Color Therapy for the Elderly (고령자를 고려한 컬러테라피 기반 색채 배색 팔레트)

  • Lee, Eun-Ji;Park, Sung-Jun
    • Journal of the Korean housing association
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    • v.28 no.1
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    • pp.55-62
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    • 2017
  • As fast-speed of aging in modern society has led to increased concern for aging and health improvement of senior citizens, desire about having healthy living-environment has also increased. Living space for senior citizens has to play role of healing for their body feature as well as decrease in mental and psychological function. Color, as important factor that supplements degenerated sense and coping ability caused by aging, it has been revealed through modern medical science that color is effective for making nervous or calming down when it is delivered to one's nerve through sight. The purpose of this study is to suggest basic resource for color arrangement palette of living space and application method by color therapy to improve seniors' mental health by considering psychological and physical features caused by aging. First, consider psychological and physical feature of seniors and color therapy effect through previous research. Second, extract RGB value after selecting color that is helpful for their mental health by using palette from 'Korea Agency for Technology and Standards'. Third, extract other 3 colors that are similar with extracted color from 'NCS 1950 Color System'. Fourth, deduct palette of 3 color arrangement by using 'NCS Navigator' program. Lastly, extract arrangement palette for them by considering difference in visual features, and then suggest arrangement application for each palette through Computer Simulation.

A Study on the Color Membership Computation Method using Fuzzy Color Model (패지 컬러 모델을 이용한 컬러의 소속 정도를 결정하는 방법에 관한 연구)

  • Kim, Dae-Won;Lee, Kwang. H.
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.04b
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    • pp.262-264
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    • 2002
  • In this paper we focused on the color representation prob1em based on fuzzy set theory. The main factor is the determination or computation of color membership function and color difference formula. The mathematical formula to calculate the color difference should generate a uniform color scaling, and due to this reason we adopted a CIELAB color- space as a fundamental feature space. With the help of the CIELAB color space we created a new color model, referred to fuzzy color model, which can represent the ambiguous characteristics underlying colors. Based on the proposed color difference formula between fuzzy colors, we could obtain the membership computation method of an arbitrary color for a given color family.

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A Technique of Feature Vector Generation for Eye Region Using Embedded Information of Various Color Spaces (다양한 색공간 정보를 이용한 눈 영역의 특징벡터 생성 기법)

  • Park, Jung-Hwan;Shin, Pan-Seop;Kim, Guk-Boh;Jung, Jong-Jin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.1
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    • pp.82-89
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    • 2015
  • The researches of image recognition have been processed traditionally. Especially, face recognition technology has been received attractions with advance and applied to various areas according as camera sensor embedded into many devices such as smart phone. In this study, we design and develop a feature vector generation technique of face for making animation caricatures using methods for face detection which are previous stage of face recognition. At first, we detect both face region and detailed eye region of component element by Viola&Johns's realtime detection method which are called as ROI(Region Of Interest). And then, we generate feature vectors of eye region by utilizing factors as opposed to the periphery and by using appearance information of eye. At this point, we focus on the embedded information in many color spaces to overcome the problems which can be occurred by using one color space. We propose a feature vector generation method using information from many color spaces. Finally, we experiment the test of feature vector generation by the proposed method with enough quantity of sample picture data and evaluate the proposed method for factors of estimating performance such as error rate, accuracy and generation time.

Content-Based Image Retrieval System using Feature Extraction of Image Objects (영상 객체의 특징 추출을 이용한 내용 기반 영상 검색 시스템)

  • Jung Seh-Hwan;Seo Kwang-Kyu
    • 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.