A New Wavelet-Based Texture Descriptor for Image Retrieval

Ves, Esther de; Ruedin, Ana M. C.; Acevedo, Daniel; Benavent, Xaro; Seijas, Leticia M.

doi:10.1007/978-3-540-74272-2_111

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…In order to model the magnitude distribution, we use the Gamma distribution which has been proved in former works to be relevant for magnitude modeling [8]. The Gamma density function is defined by…”

Section: A Model For Magnitude Distributionmentioning

confidence: 99%

“…The efficiency of the couples "model-metric" is investigated within the image-retrieval framework described in [7]. Results are compared with the ones obtained in [8].…”

Section: Introductionmentioning

confidence: 98%

“…We consider that magnitudes and orientations are independent. The same principle was used in [8] where the empirical magnitude is modeled using a Gamma distribution and the orientation is described simply using the empirical histogram. In contrast, we propose here to make use of circular models to handle orientation data.…”

Section: Introductionmentioning

confidence: 99%

“…Directional data are a kind of cyclic data in which measures are embedded on the circle such as orientations in [0, 2 [ or directions in [0, ]. Recent works [6,7,8] propose to use the first and second circular moments or the circular histogram of angles to discriminate textures. None of these works implements a complete stochastic modeling of the structural information on texture provided by orientation data.…”

Section: Introductionmentioning

confidence: 99%

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Joint linear-circular stochastic models for texture classification

Peron

Costa²,

Stitou³

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. These models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.The novelty consists in considering specific parametric models for circular data such as von Mises and ψ-distributions to describe the distributions of orientations. Particular attention is paid to the choice of a metric and to its adequation to the models. Indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the 1 L and Kullback-Leibler distances.

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Section: A Model For Magnitude Distributionmentioning

confidence: 99%

“…The efficiency of the couples "model-metric" is investigated within the image-retrieval framework described in [7]. Results are compared with the ones obtained in [8].…”

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint linear-circular stochastic models for texture classification

Peron

Costa²,

Stitou³

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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show abstract

“…They have given good results in edge detection [1] and texture identification [2]. Discrete wavelet transforms (DWT) have been used to extract features for digit recognition.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Feature Extraction for Handwritten Numerals

Romero

Ruedin

Seijas

2009

Image Analysis and Processing – ICIAP 2009

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Abstract. We present a novel preprocessing technique for handwritten numerals recognition, that relies on the extraction of multiscale features to characterize the classes. These features are obtained by means of different continuous wavelet transforms, which behave as scale-dependent bandpass filters, and give information on local orientation of the strokes. First a shape-preserving, smooth and smaller version of the digit is extracted. Second, a complementary feature vector is constructed, that captures certain properties of the digits, such as orientation, gradients and curvature at different scales. The accuracy with which the selected features describe the original digits is assessed with a neural network classifier of the multilayer perceptron (MLP) type. The proposed method gives satisfactory results, regarding the dimensionality reduction as well as the recognition rates on the testing sets of CENPARMI and MNIST databases; the recognition rate being 92.60 % for the CENPARMI database and 98.22 % for the MNIST database.

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Statistical texture retrieval in noise using complex wavelets

Rakvongthai¹,

Oraintara²

2013

Signal Processing: Image Communication

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A New Wavelet-Based Texture Descriptor for Image Retrieval

Cited by 14 publications

References 9 publications

Joint linear-circular stochastic models for texture classification

Joint linear-circular stochastic models for texture classification

Wavelet-Based Feature Extraction for Handwritten Numerals

Statistical texture retrieval in noise using complex wavelets

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