Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification

Chen, Chen; Zhou, Libing; Guo, Jianzhong; Li, Wei; Su, Hongjun; Guo, Fangda

doi:10.1109/bigmm.2015.23

Cited by 36 publications

(16 citation statements)

References 27 publications

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“…Gabor filters have ranges of frequencies and orientations that are represented according to the humans' visual system [11]. They are used in image processing for edge detection [5], texture representation [4] and texture discrimination [6]. Subsequently, they are found to be useful to process images prior to the LBP's feature extraction [12].…”

Section: The Gabor Filtermentioning

confidence: 99%

“…They can recognize and capture different scales and orientations of an image as a physical structure. Contrary, a LBP operator produces features that have detailed local descriptions [4]. It can identify and capture patterns that are invisible to Gabor filters [5].…”

Section: Introductionmentioning

confidence: 98%

“…A recent work suggested that Local Binary Pattern (LBP) method is able to handle deformation problems resulting from images acquisition and generate decent paper fingerprints [3]. However, the LBP operator filters out some texture information that can be useful in the images description [4]. In addition, the variance of the intensity values within a particular region of a paper's image is very low which makes a small noise or a change in the magnitude intensity of an image affects its description [5].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [4], proposed a Gabor-filtering-based completed local binary patterns (GCLBP) method. The GCLBP method is used for land-use scene classification in remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprinting of deformed paper images acquired by scanners

Khaleefah

Nasrudin

Mostafa

2015

2015 IEEE Student Conference on Research and Development (SCOReD)

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Images texture extraction is a core step in image pattern recognition applications such as paper texture identification or fingerprinting. Different methods are applied for paper images texture extraction. Subsequently, one of the wellknown methods in images texture extraction is the Locale Binary Pattern (LBP) method. However, the LBP method show a number of drawbacks in paper images texture extraction and two of which are neglecting some texture information of the images and incompetent to some images deformation due to its local view. In this paper, combinations of Gabor filters and a LBP operator are proposed to reduce the effects of the mentioned drawbacks in papers fingerprinting domain. We use self-collected textures from 102 paper images in the test. Consequently, the testing results of the proposed combinations improve paper images identification rate by 28.45% when the Gabor filters have a scale of 9 and an orientation of π/2 degree. This paper finds that applying Gabor filters prior to LBP method improve the LBP description and the papers fingerprinting accuracy.

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Section: The Gabor Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

“…Chen et al [4], proposed a Gabor-filtering-based completed local binary patterns (GCLBP) method. The GCLBP method is used for land-use scene classification in remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprinting of deformed paper images acquired by scanners

Khaleefah

Nasrudin

Mostafa

2015

2015 IEEE Student Conference on Research and Development (SCOReD)

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“…The WH dataset is created by labeling satellite image blocks from Google Earth by Wuhan University. It has been widely utilized in the remote sensing image scene classification task [33,[40][41][42][43]. The WH dataset comprises 19 land cover categories, each class contains 50 images with 600 × 600 pixels, and each pixel is measured in the RGB spectral space.…”

Section: Evaluation Datasetmentioning

confidence: 99%

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Zhang

Tao

et al. 2016

Remote Sensing

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Abstract:With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.Keywords: high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion

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An Efficient CBIR System for High Resolution Remote Sensing Images

Bouteldja

Kourgli

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification

Cited by 36 publications

References 27 publications

Fingerprinting of deformed paper images acquired by scanners

Fingerprinting of deformed paper images acquired by scanners

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

An Efficient CBIR System for High Resolution Remote Sensing Images

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