Texture classification using multi-resolution global and local Gabor features in pyramid space

Wang, Junmin; Fan, Yangyu; Li, Zuhe; Tao, Linmi

doi:10.1007/s11760-018-1341-6

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…The paper [24] proposed an effective image representation method based on the Gabor filtered completion local binary pattern (GCLBP) for land use scene classification. The paper [25] proposes an effective texture classification method that combines multi-resolution global and local Gabor features in pyramid space. F3DGF [26] explores phase-induced 3D Gabor based features to better utilize the two parts of Gabor features.…”

Section: Gabormentioning

confidence: 99%

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

liu,

zhang

2024

Preprint

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Grasping the situation of land use and damage in the open-pit coal mining area is of great significance to the scientific supervision and management of the area. In the existing recognition methods, the traditional features rely on manual design and the ability to express features is weak, while the deep learning methods rely too much on samples. In order to overcome the above limitations, this paper proposes a three-branch feature extraction framework that fuses deep features (DF) and shallow features (SF). Deep features mainly include two modules: key feature extraction module and contextual feature extraction module. The key feature extraction module consists of multi-level feature extraction and an attention mechanism that highlights shallow information. The new attention mechanism captures the relationship between neighboring features and adds key information from the highlighted shallow features to the final feature layer. The contextual feature extraction module introduces the Graph Convolutional Network (GCN) model to effectively reveal the correlation between the local information of the scene to obtain finer features. The shallow features are extracted by Gray-Level Co-occurrence Matrix (GLCM) to characterize the local variations of the texture, and Gabor to characterize the overall texture variations. The two kinds of features are fused and input into the particle swarm algorithm optimized support vector machine (PSO-SVM) for scene classification and recognition of remote sensing(RS) images. The method was experimented on the AID dataset and RSSCN7 dataset and the experimental results showed that the method outperforms other models.

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Section: Gabormentioning

confidence: 99%

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

liu,

zhang

2024

Preprint

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“…The orientation feature map is a combination of Oriented Gabor pyramids [41, 48]. First, orientation information

O false(θ false)

is extracted from the original image I, where

θ \in false(0 °, 45 °, 90 °, 135 ° false)

, using Gabor filter [51, 52]. Each

O false(θ false)

is used to create Gabor pyramids

O false(σ, θ false)

, where

σ

is a scale factor.…”

Section: Saliency‐based Pva Extraction Algorithmmentioning

confidence: 99%

Fusion of visual salience maps for object acquisition

Greenberg

Bensimon

Podeneshko

et al. 2020

IET Computer Vision

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The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency‐based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical‐based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well‐known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge‐coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.

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“…Thus it can acquire higher stability and recognition rate under complex illumination condition. Some classical local feature extraction algorithms mainly include Local Graphic Structure (LGS) [13], Scale Invariant Feature Transform (SIFT) [14], Local Phase Quantization (LPQ) [15], Local Derivative Pattern (LDP) [16], weighted Local Gabor (LG) [17], Local Gabor Binary Pattern (LGBP) [18], Local Differential Binary (LDB) [19], Local Linear Directional Pattern (LLDP) [20], Local Binary Pattern (LBP) [21], [22], Local Ternary Pattern (LTP) [23], Weber Local Descriptor (WLD) [24], local adapted ternary pattern (LATP) [23], [25], and centrosymmetric LTP with adaptive threshold (CS-LTPAT) [26] and more [27]- [38]. In fact, human perceptions of images depends not only on the absolute stimulus intensity, but also on the relative stimulus strength.…”

Section: Introductionmentioning

confidence: 99%

A Novel Illumination-Insensitive Feature Extraction Method

Gao

Zhang

et al. 2020

IEEE Access

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Image description is a great challenge in the field of computer vision under complex illumination condition. The complex illumination condition is usually unavoidable and unpredictable in real application. In this paper, we propose a novel generalized image descriptor, named as Anisotropy Weber Adapted Symmetric Ternary Pattern (AWASTP), which can overcome the directional inseparability of Weber Local Descriptor (WLD) and invariant threshold of Local Ternary Pattern (LTP). More particularly, we heighten the discriminative effectiveness of image description under complex illumination condition in several ways to restrain the effect of illumination variation. Firstly, a novel selection scheme for scale and angle is proposed. Based on this, an improved anisotropic Laplacian of Gaussian(ALOG) operator model is established by introducing the scale and angle parameter, moreover, an Anisotropic Weber Local Descriptor (AWLD) is presented, which can achieve more rich detailed information of illumination-insensitive feature. Secondly, an Adaptive Symmetric Ternary Pattern (ASTP) algorithm is proposed based on Weber criterion to generate more accurate threshold judgment according to the region characteristics. Thirdly, a twodimensional AWASTP histogram is created to enhance the discriminative power and represent illuminationinsensitive feature description. We conduct many experiments on benchmark databases, such as CMUPIE, FERET, PhoTex, RawFoot, and etc. Experimental results demonstrate the effectiveness of the proposed approach under different illumination conditions.

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Texture classification using multi-resolution global and local Gabor features in pyramid space

Cited by 11 publications

References 19 publications

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

Fusion of visual salience maps for object acquisition

A Novel Illumination-Insensitive Feature Extraction Method

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