Color texture image retrieval based on Gaussian copula models of Gabor wavelets

Li, Chaorong; Huang, Yuanyuan; Zhu, Lihong

doi:10.1016/j.patcog.2016.10.030

Cited by 82 publications

(48 citation statements)

References 44 publications

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“…Gaussian Copula+Gabor Wavelet [10] 92.40 EDBTC [27] 92.55 DDBTC [28] 92.65 LECoP [21] 92.99 ODII [25] 93.23 CNN-AlexNet [51] 91.34 CNN-VGG16 [51] 92.97 CNN-VGG19 [51] 93.04 Proposed LED+RD (27D)…”

Section: Methods Using Color Arr (%)mentioning

confidence: 99%

“…Per-class retrieval rate (%) on the Vistex-640 database using the proposed LED+RD method. [12] 69.40 Gaussian Copula+Weibull+ML [12] 70.60 Student-t Copula+GG+ML [12] 65.60 LEP+colorhist [19] 59.90 DDBTC [28] 44.79 LECoP [21] 74.15 Gaussian Copula+Gabor Wavelet [10] 76.40 CNN-AlexNet [51] 68.84 CNN-VGG16 [51] 74.92 CNN-VGG19 [51] 73.93 Proposed LED+RD (27D)…”

Section: Proposed Led+rd (33d)mentioning

confidence: 99%

“…were modeled by different statistical models such as GGD, the multivariate Gaussian mixture models (MGMM), Gaussian copula (GC), Student-t copula (StC), or other distributions like Gamma, Rayleigh, Weibull, Laplace, etc. to perform texture-based image retrieval [5][6][7][8][9][10]. Then, by taking into account color information within these probabilistic approaches, several studies have provided significant improvement for color image retrieval [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…For example, besides the gradient images (∇I, θ), it is interesting to add (∇I σ , θ σ ), which are generated by smoothing the image by a lowpass Gaussian filter (I σ = G σ * I) before applying the Sobel operator. Then, similar gradient features are extracted as in Equations (8)- (10). By inserting these features (computed for two extrema sets) into δ LED (p), an enhanced descriptor of dimension 33 is built.…”

mentioning

confidence: 99%

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Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

2017

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A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

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Section: Methods Using Color Arr (%)mentioning

confidence: 99%

Section: Proposed Led+rd (33d)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

2017

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“…were modeled by different statistical models such as GGD, the multivariate Gaussian mixture models (MGMM), Gaussian copula (GC), Student-t copula (StC), or other distributions like Gamma, Rayleigh, Weibull, Laplace, etc. to perform texture-based image retrieval [5,6,7,8,9,10,11]. However, one of the main drawbacks of these techniques is the their expensive computational time which has been observed an discussed in several papers [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Texture Retrieval Using Multiscale Local Extrema Descriptors and Covariance Embedding

Pham¹

2019

Lecture Notes in Computer Science

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We present an efficient method for texture retrieval using multiscale feature extraction and embedding based on the local extrema keypoints. The idea is to first represent each texture image by its local maximum and local minimum pixels. The image is then divided into regular overlapping blocks and each one is characterized by a feature vector constructed from the radiometric, geometric and structural information of its local extrema. All feature vectors are finally embedded into a covariance matrix which will be exploited for dissimilarity measurement within retrieval task. Thanks to the method's simplicity, multiscale scheme can be easily implemented to improve its scale-space representation capacity. We argue that our handcrafted features are easy to implement, fast to run but can provide very competitive performance compared to handcrafted and CNN-based learned descriptors from the literature. In particular, the proposed framework provides highly competitive retrieval rate for several texture databases including 94.95% for MIT Vistex, 79.87% for Stex, 76.15% for Outex TC-00013 and 89.74% for USPtex.

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Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval

Hao

Feng

Wei

et al. 2018

Advances in Multimedia Information Processing – PCM 2018

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Color texture image retrieval based on Gaussian copula models of Gabor wavelets

Cited by 82 publications

References 44 publications

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Efficient Texture Retrieval Using Multiscale Local Extrema Descriptors and Covariance Embedding

Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval

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