Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet

Li, Chaorong; Duan, Guiduo; Zhong, Fujin

doi:10.1109/tip.2015.2422575

Cited by 47 publications

(22 citation statements)

References 27 publications

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“…In our formulation, intermediate results of the 2-D complex Gabor filtering computed at a specific orientation can be reused when performing the 2-D complex Gabor filtering at a symmetric orientation. This is particularly useful in some applications where the 2-D complex Gabor filtering outputs at various orientations and frequencies are needed to cope with geometric variations [5], [9]- [11], [13], [14], [26]. We will show that our method reduces the computational complexity when compared to state-of-the-art methods [23], [24], while maintaining the similar filtering quality.…”

Section: Introductionmentioning

confidence: 84%

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Fast 2D Complex Gabor Filter With Kernel Decomposition

Kim

Min

2018

IEEE Trans. on Image Process.

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Abstract-2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Furthermore, we extend this idea into 2-D localized sliding discrete Fourier transform (SDFT) using the Gaussian kernel in the DFT computation, which lends a spatial localization ability as in the 2-D complex Gabor filter. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.Index Terms-2-D complex Gabor filter, 2-D complex Gabor filter bank, 2-D localized sliding discrete Fourier transform (SDFT), kernel decomposition.

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Section: Introductionmentioning

confidence: 84%

“…found a great variety of applications in the field of computer vision and image processing, including texture analysis [6]- [9], face recognition [10]- [14], face expression recognition [15], [16] and fingerprint recognition [17].…”

Section: Introductionmentioning

confidence: 99%

Fast 2D Complex Gabor Filter With Kernel Decomposition

Kim

Min

2018

IEEE Trans. on Image Process.

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“…The second remark is that, as previously mentioned in the introduction of our article, the local patterns-based schemes (such as LtrP [17], LECoP [21], etc.) and the OTB-based systems [26][27][28] as well as the recently proposed learned descriptors based on pre-trained CNNs [50,51] generally provide higher ARR than the wavelet-based probabilistic approaches [4][5][6][7][8][9]11,12,29]. Then, more importantly, our LED+RD framework (both the 27D version and the improved 33D version) has outperformed all reference methods for all the three databases.…”

Section: Performance In Retrieval Accuracymentioning

confidence: 90%

“…Due to the fact that natural images usually involve a variety of local textures and structures that do not appear homogeneous within the entire image, an approach taking into account local features could become relevant. This may be the reason why most local feature-based CBIR schemes (e.g., LTrP [17], LOCTP [20], LECoP [21]) or BTC-based approaches [25][26][27][28] (i.e., which, in fact, sub-divide each query image into multiple blocks) have achieved better retrieval performance than probabilistic methods, which model the entire image using different statistical distributions [4][5][6][7][8][9]11,12,29]. We will provide later their performance for a comparison within the experimental study.…”

Section: Introductionmentioning

confidence: 99%

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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“…Thakare and Patil [7] presented an improved method for texture image classification and retrieval using gray level cooccurrence matrix (GLCM) and self-organizing maps (SOM). Riaz et al [8] and Li et al [9] introduced a novel technique to rotation and scale invariant texture classification based on Gabor wavelet feature that have the capability to collapse the filter responses according to the scale and orientation of the texture features. Liu et al [10] and Zhao et al [11] presented a novel approach for texture feature classification by generalizing the well-known local binary pattern (LBP) approach.…”

Section: Introductionmentioning

confidence: 99%

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

Wan

Wang

et al. 2016

Computational Intelligence and Neuroscience

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Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

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Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet

Cited by 47 publications

References 27 publications

Fast 2D Complex Gabor Filter With Kernel Decomposition

Fast 2D Complex Gabor Filter With Kernel Decomposition

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

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

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