Color quantization and its impact on color histogram based image retrieval accuracy

Meskaldji, Khouloud; Boucherkha, Samia; Chikhi, Salim

doi:10.1109/ndt.2009.5272135

Cited by 28 publications

(11 citation statements)

References 4 publications

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“…This color histogram combines red, green and blue colors to form a new color. To form a new color it is necessary to increase the values of one or more of the components of the RGB components [9].…”

Section: Rgb Histogrammentioning

confidence: 99%

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Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Figueroa

Bykbaev

2012

ings

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In this paper we present the first stage of a new approach to improve the precision and recall of the content-based image retrieval task. To do this, we aim to combine three colour features, RGB and HSV histograms, and MPEG-7 Colour Layout Descriptor. To perform the combination, we propose to use an approximation based on Borda Voting-Schemes. Under that the Borda Voting-Schemes needs at least three votes to perform the combination, we intend to use the K-Nearest Neighbors methods to select the candidate images, given a query image. In the second stage, we'll implement our approach using at least three image databases.Keywords: HSV histogram, RGB histogram, Colour layout fescriptor, Borda Voting Schemes, KNN. ResumenEn este artículo se presenta la primera fase de experimentación de una nueva propuesta para mejorar la precisión y cobertura de la recuperación de imágenes basada en contenidos. Para realizar esta tarea, se propone combinar tres características de color, como son los histogramas RGB y HSV y el Descriptor de Distribución de Color del estándar MPEG-7. A fin de llevar a cabo la combinación, se plantea emplear una aproximación basada en Esquemas de Votación Borda. En virtud de que dichos esquemas requieren al menos tres votos, se propone usar la técnica de los K -Vecinos más cercanos, con el objetivo de seleccionar las imágenes candidatas a partir de imagen de consulta. En la segunda etapa se implementará nuestra propuesta empleando al menos tres bases de datos de imágenes.

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Section: Rgb Histogrammentioning

confidence: 99%

“…As it shows a new color can be formed by changing the values of the RGB component. For example, if the magenta color is needed, it is necessary to combine green and blue to create it [9]. RGB coordinate system where the "x" axis represents red colors, "y" axis represents green colors and "z" represents blue colors [10].…”

Section: Rgb Histogrammentioning

confidence: 99%

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Figueroa

Bykbaev

2012

ings

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“…Image feature extraction can be based on various attributes, i.e. color [3], texture [4], shape [5,6], local features [7], multiresolution wavelet analysis [8][9][10][11] and their improvements [12,13]. It seems to be interesting to develop a novel method for object description.…”

Section: Introductionmentioning

confidence: 99%

New image descriptor from edge detector and blob extractor

Grycuk¹,

Scherer²,

Gabryel³

2015

J. Appl. Math. Comput. Mech.

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Abstract. In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.

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“…Thus, to calculate a 699 RGB histogram-based image similarity, it is important to obtain a good colour representation of 700 the image by uniformly sampling the RGB space. Then, the standard 216 RGB colour palette 701 can be used [53], and it has been the one selected in this comparison.…”

mentioning

confidence: 99%

“…For each art composition in Figure 8, the quantised RGB colour histograms has been ob-707 tained and the Euclidean distance between these RGB histograms has been computed [53] and 708 normalised (see the Appendix), which is denoted by SimRGB. Then, SimRGB and SimQCDI 709 are compared in order to analyse which of these methods is closer to the results of the survey 710 described previously in Section 10.4:…”

mentioning

confidence: 99%

A model for colour naming and comparing based on conceptual neighbourhood. An application for comparing art compositions

Falomir¹,

Museros²,

Abril³

2015

Knowledge-Based Systems

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A computational model for Qualitative Colour Description, named the QCD model, is defined using the Hue, Saturation and Luminance colour space. This model can name rainbow colours, pale, light and dark colours, and colours in the grey scale, and it has been parameterised by participants of a study in two universities in Spain: University Jaume I and University of Sevilla. The relational structure of the QCD model is analysed by means of a conceptual neighbourhood diagram and it is used to formulate a measure of similarity for solving absolute and relative comparisons of qualitative colours. Moreover, a similarity measure between colour compositions, called SimQCDI, is also developed. A survey test on several art compositions is carried out and the results obtained by the participants are analysed and compared to the computational results provided by the SimQCDI. Also, a comparison to the standard RGB Colour Histogram similarity method is carried out, which shows that the proposed similarity is more intuitive and that the results obtained are similar with respect to quantification. Finally, the cognitive adequacy of the QCD model is also analysed.

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Color quantization and its impact on color histogram based image retrieval accuracy

Cited by 28 publications

References 4 publications

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

New image descriptor from edge detector and blob extractor

A model for colour naming and comparing based on conceptual neighbourhood. An application for comparing art compositions

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