Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features

Rasti, Javad; Monadjemi, Amirhassan; Vafaei, Abbas

doi:10.1016/j.eswa.2011.04.132

Cited by 23 publications

(11 citation statements)

References 28 publications

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“…Image dependent methods generally provide good results, but are a bit more expensive. Image dependent methods can be divided in preclustering methods, e.g., (Heckbert, 1982), (Gervautz and Purgathofer, 1990), (Wu, 1992), (Kanjanawanishkul and Uyyanonvara, 2005), and post-clustering methods, e.g., (Ozdemir and Akarun, 2002), (Bing et al, 2004), (Kim and Kehtarnavaz, 2005), (Atsalakis and Papamarkos, 2006), (Chen et al, 2008), (Ramella and Sanniti di Baja, 2010), (Rasti et al, 2011), (Celebi 2011). Pre-clustering methods determine only once the color palette by using features derived from the image at hand.…”

Section: Introductionmentioning

confidence: 99%

Color Quantization via Spatial Resolution Reduction

Ramella¹,

Baja

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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A color quantization algorithm is presented, which is based on the reduction of the spatial resolution of the input image. The maximum number of colors n f desired for the output image is used to fix the proper spatial resolution reduction factor. This is used to build a lower resolution version of the input image with size n f. Colors found in the lower resolution image constitute the palette for the output image. The three components of each color of the palette are interpreted as the coordinates of a voxel in the 3D discrete space. The Voronoi Diagram of the set of voxels corresponding to the colors of the palette is computed and is used for color mapping of the input image.

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Section: Introductionmentioning

confidence: 99%

Color Quantization via Spatial Resolution Reduction

Ramella¹,

Baja

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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“…A color in the HSV color space is represented by Hue: which is used to distinguish colors and it also represents the nature of the color; Saturation: measures the degree to which a pure color is mixed by white light; and the Value: represents the perceived light intensity [17]. Zhao et al [18] indicated that the chromaticity component in HSV color pace is separated from the brightness; such that the Value component represents the brightness while Hue and Saturation components represent the chromaticity.…”

Section: Discrete Wavelet Transformation (Dwt) Decomposes (Analyzes) mentioning

confidence: 99%

Content-Based Image Retrieval Using SOM and DWT

Huneiti¹,

Daoud²

2015

JSEA

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Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via SOM. The proposed method demonstrated promising retrieval results on the Wang Database compared to the existing methods in literature.

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“…The relevant issue in most such applications is to retain the meaning of the data as much as possible while reducing its size. This requirement led to several optimal methods, which employed self-organizing maps [3], ant colony [4], Fibonacci lattices [5], self growing and self organized neural gas [6], superposed histogram based adaptive clustering [7], dynamic programming [8], modified fuzzy c-means clustering [9], improved hard c-means clustering [10], and histogram-based c-means clustering [2]. Besides effective storage and communication, the most relevant applications of color reduction are in image segmentation [11] [12], [13] and document analysis [14].…”

Section: Introductionmentioning

confidence: 99%

Comparison of various improved-partition fuzzy c-means clustering algorithms in fast color reduction

Szilágyi

Dénesi

Kovács

et al. 2014

2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY)

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Abstract-This paper provides a comparative study of several enhanced versions of the fuzzy c-means clustering algorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clustering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested c-means algorithms. Clustering is followed by another common step, which creates the output image. Besides conventional hard (HCM) and fuzzy c-means (FCM) clustering, the so-called generalized improved partition FCM algorithm, and several versions of the suppressed FCM (s-FCM) in its conventional and generalized form, are included in this study. Accuracy is measured as the average color difference between pixels of the input and output image, while efficiency is mostly characterized by the total runtime of the performed color reduction. Numerical evaluation found all enhanced FCM algorithms more accurate, and four out of seven enhanced algorithms faster than FCM. All tested algorithms can create reduced color images of acceptable quality.

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Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features

Cited by 23 publications

References 28 publications

Color Quantization via Spatial Resolution Reduction

Color Quantization via Spatial Resolution Reduction

Content-Based Image Retrieval Using SOM and DWT

Comparison of various improved-partition fuzzy c-means clustering algorithms in fast color reduction

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