Relevant bibliographies by topics / Color-vector clustering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Color-vector clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Color-vector clustering"

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Qi, Li Ying, and Ke Gang Wang. "Information System in Image Classification Based on SVM and Color Clustering Analysis." Advanced Materials Research 886 (January 2014): 572–75. http://dx.doi.org/10.4028/www.scientific.net/amr.886.572.

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Effective use of the color feature of Content Based Image Retrieval (CBIR) and Image classification is an important basic research, but there are some shortcomings in the color histogram representation method, such as high dimension, pixels spatial information is ignored and so on. Although color feature data can reduce the dimension by quantification, but some useful image color information will be discard. In this paper, the image color information processing in space constrained fuzzy clustering to obtain a lower dimensional color feature data of the image characteristics of domain colors description, and use multi-class support vector machine to classify color images. Experimental results show that the proposed method can better describe image color information than color histogram; image domain color description combined with support vector machine model can achieve the automatic classification of images effectively.
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Putra, I. Ketut Gede Darma, Ni Putu Ayu Oka Wiastini, Kadek Suar Wibawa, and I. Made Suwija Putra. "Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine." International Journal of Machine Learning and Computing 10, no. 4 (July 2020): 542–48. http://dx.doi.org/10.18178/ijmlc.2020.10.4.970.

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Putra, I. Ketut Gede Darma, Ni Putu Ayu Oka Wiastini, Kadek Suar Wibawa, and I. Made Suwija Putra. "Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine." International Journal of Machine Learning and Computing 10, no. 5 (October 5, 2020): 700–706. http://dx.doi.org/10.18178/ijmlc.2020.10.5.993.

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Liu, Jin Mei, and Ji Zhong Li. "Image Retrieval Based on Color-Statistic Feature." Advanced Materials Research 765-767 (September 2013): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1046.

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Color is the most widely used visual feature in content based image retrieval. The visual coherence color space, HSV, is adopted to represent image. Hue component is used to denote color. Hue difference statistic is proposed to extract color change information as supplement to color feature. The image is divided into sub images equally. Color and change information is extracted in each region. After feature vector clustering and coding, image content can be expressed as vector codes. The text based analysis technology is used for image retrieval. Experiments show that the proposed method can realize efficient retrieval for unconstrained scene images.
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Qiao, Yu-Long, Kai-Long Yuan, Chun-Yan Song, and Xue-Zhi Xiang. "Detection of Moving Objects with Fuzzy Color Coherence Vector." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/138065.

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Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.
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Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "An Adaptive Image Segmentation Algorithm Based on AP Clustering." Advanced Materials Research 1078 (December 2014): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.405.

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For the shortcomings of classsical clustering algorithms: must assign the number of clusters, the initial cluster center and initial membership degree matrix, ignore the spatial structure information, we propose a novel adaptive image segmentation algorithm based on AP clustering (AAP). We calculate the AP clustering preference parameter of different images adaptively, use the color-texture feature vector to clustering segmentation. Compare to K-Means and FCM, the new algorithm is more accurate and robust.
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Phornphatcharaphong, Wutthichai, and Nawapak Eua-Anant. "Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images." Journal of Imaging 6, no. 7 (July 16, 2020): 72. http://dx.doi.org/10.3390/jimaging6070072.

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This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.
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Lin, Ye, Dan Chen, Shijia Liang, Zhezhuang Xu, Yang Qiu, Jiahao Zhang, and Xinxiang Liu. "Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm." Applied Sciences 10, no. 19 (September 29, 2020): 6816. http://dx.doi.org/10.3390/app10196816.

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Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.
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Xu, Yan, Jiangtao Dong, Zishuo Han, and Peiguang Wang. "Multichannel Correlation Clustering Target Detection." Information Technology And Control 49, no. 3 (September 23, 2020): 335–45. http://dx.doi.org/10.5755/j01.itc.49.3.25507.

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During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.
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Fang, Lu Ping, Yuan Jie Wei, and Fei Lu. "Detection of Color Indicators under Complex Circumstances." Advanced Materials Research 433-440 (January 2012): 6157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6157.

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A color indicator detection algorithm under different illumination conditions is proposed. First, based on the similarity between consecutive video frames in channel L of Lab color space, background image can be determined. Differentiation of a frame and the background can identify the motion region, and thus the search area for the color indicator is greatly reduced. Second, the convex hull of motion region is specified and sampling is taken within it. By assigning the weight, seeds can be determined using clustering method. Finally, region growing is implemented by applying Bayesian decision with minimal error ratio. The method is applicable to more different conditions and produces better results compared with traditional color-threshold vector method.
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Dissertations / Theses on the topic "Color-vector clustering"

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Karlsson, Fredrik. "Matting of Natural Image Sequences using Bayesian Statistics." Thesis, Linköping University, Department of Science and Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2355.

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The problem of separating a non-rectangular foreground image from a background image is a classical problem in image processing and analysis, known as matting or keying. A common example is a film frame where an actor is extracted from the background to later be placed on a different background. Compositing of these objects against a new background is one of the most common operations in the creation of visual effects. When the original background is of non-constant color the matting becomes an under determined problem, for which a unique solution cannot be found.

This thesis describes a framework for computing mattes from images with backgrounds of non-constant color, using Bayesian statistics. Foreground and background color distributions are modeled as oriented Gaussians and optimal color and opacity values are determined using a maximum a posteriori approach. Together with information from optical flow algorithms, the framework produces mattes for image sequences without needing user input for each frame.

The approach used in this thesis differs from previous research in a few areas. The optimal order of processing is determined in a different way and sampling of color values is changed to work more efficiently on high-resolution images. Finally a gradient-guided local smoothness constraint can optionally be used to improve results for cases where the normal technique produces poor results.

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Micenková, Barbora. "Ověření pravosti razítek v dokumentu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236943.

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Klasická inkoustová razítka, která se používají k autorizaci dokumentů, se dnes díky rozšíření moderních technologií dají relativně snadno padělat metodou oskenování a vytištění. V rámci diplomové práce je vyvíjen automatický nástroj pro ověření pravosti razítek, který najde využití zejména v prostředích, kde je nutné zpracovávat velké množství dokumentů. Procesu ověření pravosti razítka musí přirozeně předcházet jeho detekce v dokumentu - úloha zpracování obrazu, která zatím nemá přesvědčivé řešení. V této diplomové práci je navržena zcela nová metoda detekce a ověření pravosti razítka v barevných obrazech dokumentů. Tato metoda zahrnuje plnou segmentaci stránky za účelem určení kandidátních řešení, dále extrakci příznaků a následnou klasifikaci kandidátů za pomoci algoritmu podpůrných vektorů (SVM). Evaluace ukázala, že algoritmus umožňuje rozlišovat razítka od jiných barevných objektů v dokumentu jako jsou například loga a barevné nápisy. Kromě toho algoritmus dokáže rozlišit pravá razítka od kopií.
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Veľas, Martin. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236399.

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This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.
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Book chapters on the topic "Color-vector clustering"

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Dubey, Shiv Ram, and Anand Singh Jalal. "Automatic Fruit Disease Classification Using Images." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 82–100. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6030-4.ch005.

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Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.
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Conference papers on the topic "Color-vector clustering"

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Zhang, Quan, Xiaoying Tai, Yihong Dong, Shanliang Pan, Xiaoquan Wang, and Caoqian Yin. "Improved Color Clustering Vector Applied in Endoscope Image Retrieval." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.982.

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Zhan, Qingming, Liang Yu, and Yubing Liang. "A point cloud segmentation method based on vector estimation and color clustering." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691038.

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Kita, Kohei, and Toru Wakahara. "Binarization of Color Characters in Scene Images Using k-means Clustering and Support Vector Machines." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.779.

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Wakahara, Toru, and Kohei Kita. "Binarization of Color Character Strings in Scene Images Using K-Means Clustering and Support Vector Machines." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.63.

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Mohammed, Emad A., Behrouz H. Far, Mostafa M. A. Mohamed, and Christopher Naugler. "Application of Support Vector Machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation." In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013). IEEE, 2013. http://dx.doi.org/10.1109/healthcom.2013.6720751.

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