221,821 research outputs found

    Color Image Clustering using Block Truncation Algorithm

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    With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

    Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

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    We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

    A Performance Analysis of the Faugeras Color Space as a Component of Color Histogram-Based Image Retrieval

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    The use of color histograms for image retrieval from databases has been implemented in many variations. Selecting the appropriate color space for similarity comparisons is an important part of a color histogram technique. This paper serves to introduce and evaluate the performance of a color space through the use of color histograms. Performance is evaluated by correlating the similarity results obtained from various color feature vector techniques (including color histgramming) to those gathered through a human perceptual test. The perceptual test required 36 human subjects to evaluate the similarity of 10 military aircraft images. The same 10 images were also compared via the color feature vector techniques. The results obtained for the Faugeras color space are compared against those of the Red, Green, Blue (RGB) and Hue, Saturation, Value (HSV) color spaces. While the correlation results for the Faugeras color space were unexpected and unfavorable, a Pearson correlation coefficient of 0.91 was obtained for the HSV space suggesting that HSV is an excellent color space for judging color image similarity. A discussion of the Faugeras space\u27s performance and future research directions are presented at the conclusion of the paper

    Perceptual similarity between color images using fuzzy metrics

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    “NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Visual Communication and Image Representation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Visual Communication and Image Representation, [Volume 34, January 2016, Pages 230–235] https://doi.org/10.1016/j.jvcir.2015.04.003In many applications of the computer vision field measuring the similarity between (color) images is of paramount importance. However, the commonly used pixelwise similarity measures such as Mean Absolute Error, Peak Signal to Noise Ratio, Mean Squared Error or Normalized Color Difference do not match well with perceptual similarity. Recently, it has been proposed a method for gray-scale image similarity that correlates quite well with the perceptual similarity and it has been extended to color images. In this paper we use the basic ideas in this recent work to propose an alternative method based on fuzzy metrics for perceptual color image similarity. Experimental results employing a survey of observations show that the global performance of our proposal is competitive with best state of the art methods and that it shows some advantages in performance for images with low correlation among some image channels. (C) 2015 Elsevier Inc. All rights reserved.Grecova, S.; Morillas Gómez, S. (2016). Perceptual similarity between color images using fuzzy metrics. Journal of Visual Communication and Image Representation. 34:230-235. doi:10.1016/j.jvcir.2015.04.003S2302353

    Gray Image Colorization using Thepade’s Transform Error Vector Rotation With Cosine, Walsh, Haar Transforms and various Similarity Measures

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    The paper presents various gray image colorization methods based on vector quantization for performing automatic colorization. To colorize gray target image by extracting color pixels from source color image, Thepade’s Transform Error Vector Rotation vector quantization methods such as Thepade’s Cosine Error Vector Rotation (TCEVR), Thepade’s Walsh Error Vector Rotation (TWEVR) and Thepade’s Haar Error Vector Rotation (THEVR) are used along with varied similarity measures. The quality of colorization of gray image is subjective to the source color image and target gray image (to be colored). Here the image test bed of 25 images is used to recolor the gray equivalent of the original color images for qualitative performance comparison of proposed colorization methods with help of PSNR between original color and recolored images. Colorization is performed using diverse similarity measures which belong to different families. These nine similarity measures are used for mapping gray image pixels with relatively corresponding multichorme image pixels. When these similarity measures are assessed for their comparison for colorizing the target gray image, it is observed that Chebychev outruns all other similarity measures and the worst performance is consistently given by Jaccard and Hamming distances. Among all the considered colorization methods Thepade’s Haar Error Vector Rotation is much suitable algorithm for performing gray image colorization. DOI: 10.17762/ijritcc2321-8169.150516
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