Laguerre Gauss analysis for image retrieval based on color texture

Costantini, Luca; Sità, Paolo; Capodiferro, Licia; Neri, Alessandro

doi:10.1117/12.843838

Cited by 8 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Laguerre-Gauss functions are defined by two indexes, n and k which are related to the angular and the radial order, respectively, and a parameter related to the resolution. In this context we use the Laguerre-Gauss with n=1 and k=0, and n=3 and k=0 at three different resolution, low, medium and high [9] . The marginal densities of the Laguerre-Gauss expansion coefficients are well approximated by the Generalized Gaussian Densities (GGDs), that are defined by two parameters α and β .…”

Section: Texture Featurementioning

confidence: 99%

See 1 more Smart Citation

Features extraction based on Fisher's information

et al. 2011

View full text Add to dashboard Cite

"In this paper a novel scheme for extracting the global features from an image. Usually the features are extracted from the whole image. In the proposed approach, only the image regions conveying information are considered. The two steps procedure is based on the Fisher's information evaluation computed by linear combination of Zernike expansion coefficients. Then, by using the region growing algorithm, only high information rate regions are considered. The considered features are texture, edges, and color. The performances of the proposed scheme has been evaluated by using the retrieval rate. Experimental results show an increase in the retrieval rate with respect to use the same features computed on whole image.

show abstract

Section: Texture Featurementioning

confidence: 99%

“…The mean, the variance, and the third centered moment are evaluated on the chromatic components [9] . In this way the information about the color is represented by a vector of six elements.…”

Section: Color Featurementioning

confidence: 99%

Features extraction based on Fisher's information

et al. 2011

View full text Add to dashboard Cite

show abstract

“…With the aim of describe the texture the marginal densities of the real part and of the imaginary part of D k n N r ξ , they are separately approximated by two Generalized Gaussian Densities (GGDs). 8,10 The GGDs can be completely described by two parameters α and β. The first one is directly related to the width of the density, while the second one is inversely proportional to the decreasing rate of the density.…”

Section: Texture Segmentationmentioning

confidence: 99%

“…5 The proposed algorithm is based on the idea that textured areas within an image can be detected by analyzing the shape of the marginal density of the Laguerre Gauss expansion coefficients, as well as Pyka et al 6 make use of the kurtosis to detect the noisy regions. The marginal densities of the wavelet, 7,8 and of the Laguerre Gauss coefficients, 9,10 are widely used to classify the textures. Since in this work we are interested in the detection of the textured ares in natural images, we analyze the local marginal densities.…”

Section: Introductionmentioning

confidence: 99%

“…To this aim, for each pixel we collect the neighbor pixels labeled as texture and we characterize this patch by approximating the marginal densities of the Laguerre Gauss coefficients as Generalized Gaussian Densities (GGDs). 8,10 Then, the K-means 11 clustering algorithm is used for segmenting the textured areas. 12 The metric used in the clustering algorithm is a symmetric version of Kullback-Leibler distance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Textured areas detection and segmentation in circular harmonic functions domain

Costantini

Capodiferro

Carli

et al. 2012

Image Processing: Algorithms and Systems X; And Parallel Processing for Imaging Applications II

Self Cite

View full text Add to dashboard Cite

In this work a novel technique for detecting and segmenting textured areas in natural images is presented. The method is based on the circular harmonic function, and, in particular, on the Laguerre Gauss functions. The detection of the textured areas is performed by analyzing the mean, the mode, and the skewness of the marginal densities of the Laguerre Gauss coefficients. By using these parameters a classification of the patch and of the pixel, is performed. The feature vectors representing the textures are built using the parameters of the Generalized Gaussian Densities that approximate the marginal densities of the Laguerre Gauss functions computed at three different resolutions. The feature vectors are clustered by using the K-means algorithm in which the symmetric Kullback-Leibler distance is adopted. The experimental results, obtained by using a set of natural images, show the effectiveness of the proposed technique.

show abstract