Histogram ratio features for color texture classification

Paschos, George; Petrou, Maria

doi:10.1016/s0167-8655(02)00244-1

Cited by 46 publications

(25 citation statements)

References 11 publications

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“…Segmentation of color images by taking into account the interaction between color and spatial frequency of patterns was proposed by Mirmehdi and Petrou [6]. An interesting approach to combining color and texture with histogram ratio features was recently published by Paschos and Petrou [7]. For a related work on color constant ratio gradients, see Ref.…”

Section: Introductionmentioning

confidence: 99%

Classification with color and texture: jointly or separately?

Mäenpää

Pietikäinen

2004

Pattern Recognition

270

167

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Section: Introductionmentioning

confidence: 99%

Classification with color and texture: jointly or separately?

Mäenpää

Pietikäinen

2004

Pattern Recognition

270

167

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“…In the sequence, to provide a better evaluation of the proposed signatures, we performed a comparison with other color texture methods found in the literature. For this comparison, we considered the following methods: Gabor EEE [53,54], Histogram ratio features (HRF) [55], MultiLayer CCR [56], Linear prediction model [57,46] and an LBP þHaralick method [48]. A brief description of each method is presented as follows.…”

Section: Discussionmentioning

confidence: 99%

Color texture analysis based on fractal descriptors

Backes

Casanova

Bruno

2012

Pattern Recognition

156

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“…The results obtained were compared with the use of other classical methods for descriptors of colored textures, that is, color Gabor, 24 color histogram ratio, 28 and chromaticity moments. 22 The descriptors are classified by the well known K-Nearest Neighbor (KNN) method, 29 with k ¼ 1 (empirically determined) and using a 10-fold cross-validation process.…”

Section: Methodsmentioning

confidence: 99%

Fractal descriptors in the Fourier domain applied to color texture analysis

Florindo¹,

Bruno²

2011

Chaos

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A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data Rev. Sci. Instrum. 82, 015105 (2011) Support vector machine-based feature extractor for L/H transitions in JET Rev. Sci. Instrum. 81, 10E123 (2010) Determining the absorption tolerance of single chromophore photodiodes for machine vision Appl. Phys. Lett. 96, 253303 (2010) Determining the absorption tolerance of single chromophore photodiodes for machine vision APL: Org. Electron. Photonics 3, 123 (2010) The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists of two steps. First, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. Second, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors that are used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach. Fractal objects constitute a particular category of nonlinear dynamic system. Essentially, fractals are geometric objects which do not obey the classical Euclidian rules. Besides, they are characterized by the self-similarity, that is, we observe some geometrical and/or statistical patterns which repeat themselves along different observation scales. In a true fractal, the self-similarity is observed at infinite observation scales. The inherent composition rules of fractals yield to some interesting characteristics. For instance, fractals present non-proportionality between cause and effect. Still they present infinite complexity (complexity in the sense of observed details) and they show a high dependence level from initial conditions. Such characteristics allow for fractals are also to be considered as a chaotic system. This physical interpretation collaborated for authors, such as Mandelbrot, 1 to suggest the use of fractals to model objects found in the nature. More recently, Manoel et al. 2 developed a method for using fractal theory in the extraction of features from natural objects represented in a digital image. This is an important problem in computational vision. Bruno et al. 3 applied the technique to a shape recognition problem. Here, we propose the development and study of a novel fractal-based method for the analysis of color texture. We verify the efficiency of the proposed approach in the solution of a color texture classification problem. Additional information on Chaos

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Histogram ratio features for color texture classification

Cited by 46 publications

References 11 publications

Classification with color and texture: jointly or separately?

Classification with color and texture: jointly or separately?

Color texture analysis based on fractal descriptors

Fractal descriptors in the Fourier domain applied to color texture analysis

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