Statistical texture retrieval in noise using complex wavelets

Rakvongthai, Yothin; Oraintara, S.

doi:10.1016/j.image.2013.06.005

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…In this work, the rotation and scale invariant properties are also validated for 12 directions and 5 scales in standard texture image database. Rakvongthai et al [17] have computed texture feature descriptor in the noisy environment based on the complex wavelets using the statistical values where each sub-band coefficients are modeled by the standard statistical distribution. To improve the retrieval accuracy, they have combined the magnitude and phase information of complex sub-band coefficients.…”

Section: B Texture Visual Feature Descriptorsmentioning

confidence: 99%

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

et al. 2020

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In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In Initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

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Section: B Texture Visual Feature Descriptorsmentioning

confidence: 99%

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

et al. 2020

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“…Although our main contribution concerns magnitudes of complex wavelet coefficients, another experiment on influence of the information from angles has been conducted on the three datasets. To capture this information, we use von Mises distribution to characterize histograms of the relative phase as in [22], [23], and [25]. This joint modeling requires some changes in the similarity measurement.…”

Section: E Influence Of Phase Informationmentioning

confidence: 99%

“…Since magnitudes are positive values, it is obvious that all of this positive distributions are more suitable than a GGD to take advantage of the exponential family distributions and model the marginal behavior of complex wavelet coefficients histograms as well. Additionally, Weibull distribution has been successfully used to model magnitudes of complex wavelet coefficients in some previous texture retrieval models [22], [24], and [26]- [28]. Angles of complex coefficients are generally exploited using circular distributions such as Wrapped Cauchy and von Mises to characterize the relative phase [29], [30].…”

Section: Introductionmentioning

confidence: 99%

A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

Rami

Maliani

2019

IEEE Access

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This paper presents a new statistical model for texture retrieval in the complex wavelet domain. For this purpose, a finite mixture of Weibull distributions (MoWbl) is proposed to characterize the statistical distribution of magnitudes of complex wavelet coefficients. Despite the ability of the mixture model on capturing a wide range of distribution shapes, choosing an appropriate number of mixture components is a challenging task. To this end, we adopt an unsupervised learning of the model parameters based on the Figueiredo-Jain algorithm and maximum-likelihood estimates. As found in all retrieval statistical-based frameworks, the presence of a similarity measure is trivial. Generally, the failure of a retrieval mixture based system is closely related to the choice of the similarity measure that relies mainly on approximations of some divergences and distances. To overcome this limitation, we propose a canonical form of Weibull distribution which allows us to develop an analytic expression of Cauchy-Schwarz divergence (CSD) for MoWbl distributions. Experiments, conducted on three popular datasets, show that the proposed model yields better performance in terms of goodness-of-fit, retrieval, and execution time compared to some related statistical models for texture retrieval.INDEX TERMS Texture retrieval, statistical analysis, finite mixture of weibulls, Cauchy-Schwarz divergence.

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“…Each subband is composed by different complex numbers and it is shift invariant. We study in this paper the relative phases of these complex numbers [18]. From each complex numbers z = ai + b, the phase φ can be calculated as follows:…”

Section: A Complex Wavelet Decompositionmentioning

confidence: 99%

Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification

Karine

Toumi

Khenchaf

2018

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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In this paper, we present a novel approach for radar automatic target recognition on inverse synthetic aperture radar (ISAR). This approach is composed by two complementary steps: feature extraction and recognition. For the feature extraction step, we adopt a statistical modeling of the ISAR image in the complex wavelet domain. For doing so, we apply the dual-tree complex wavelet transform (DT-CWT) for each ISAR image in the database. After that, the relative phases of the resulting complex coefficients are computed. These relative phases are after statistically modeled using the Von Mises distribution. The estimated statistical parameters compose the feature vector of the ISAR images. Regarding to the recognition rate, the constructed feature vector is fed into the sparse representation based classification (SRC). More precisely, the training feature vectors are used as the atoms of a dictionary. The test feature vector is recognized according to its sparse linear combination with the dictionary. Extensive experiments and comparisons with other methods on ISAR images database demonstrate the effectiveness of the proposed approach.

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Statistical texture retrieval in noise using complex wavelets

Cited by 12 publications

References 35 publications

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification

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