Noise-invariant structure pattern for image texture classification and retrieval

Shrivastava, Nishant; Tyagi, Vipin

doi:10.1007/s11042-015-2811-2

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…To improve noise-robustness, some CLBP variants such as AECLBP [8], CRLBP [36] and CNLP [25] are proposed successively. The key idea of these descriptors is to replace the noise-sensitive threshold of centre pixel value with a more robust compositional variable.…”

Section: Review Of Cnlpmentioning

confidence: 99%

“…4 is not limited to original CLBP (its improved version names ICLBP). Any other CLBP-like variant such as CRLBP [36] or CNLP [25] can be strengthened to ICRLBP or ICNLP, respectively. Second, GCLBP-series descriptors completely maintain the original functions.…”

Section: A Characteristics Of Gclbp-series Descriptorsmentioning

confidence: 99%

“…This paper investigated that the local binary patterns (LBP) descriptor is lightweight and efficient for texture representation [14]- [16]. This descriptor and its various variants, such as completed LBP (CLBP) [24], completed noise-invariant local-structure pattern (CNLP) [25] and dominant LBP (DLBP) [26], have been successfully applied to face recognition [17]- [19], texture classification [20], [21] and visual tracking [22]. Some literature on surface defect inspection based on LBP are reported [8], [23].…”

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confidence: 99%

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Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo

Sun

et al. 2019

IEEE Trans. Instrum. Meas.

129

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Abstract-Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intra-class variation, ambiguous inter-class distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional LBP variants, descriptive information hidden in nonuniform patterns is innovatively excavated for better defect representation. This work focuses on the following aspects: First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest neighbor classifier (NNC). The classification accuracy and time-efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database (NEU). The experimental results promise that the proposed method can be widely applied in online AOI instruments for hot-rolled strip steel.Index Terms-Surface defects, image classification, hot-rolled strips, local binary patterns (LBP), automatic optical inspection (AOI) instrument.

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Section: Review Of Cnlpmentioning

confidence: 99%

Section: A Characteristics Of Gclbp-series Descriptorsmentioning

confidence: 99%

mentioning

confidence: 99%

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Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo

Sun

et al. 2019

IEEE Trans. Instrum. Meas.

129

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“…Tiwari et al [27] proposed an improved Weber's law based local binary pattern with an application to dynamic texture recognition. Shrivastava and Tyagi [25] proposed a noise invariant structure pattern (NISP) that uses both the center pixel and local and global information to represent an image.…”

Section: Introductionmentioning

confidence: 99%

Multi-view texture classification using hierarchical synthetic images

Zhang

Liang

2016

Multimed Tools Appl

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Multi-view texture classification is a very challenging task since the view-point variation often leads to the inconsistent local texton patterns. Existing studies focus on the extraction of scale, rotation or affine invariant representations by some specially designed invariant measurements or local descriptors. Differently, in this paper, we propose another framework for multi-view texture classification. A number of synthetic images are hierarchically created to enlarge the training dataset to cover the possible variations of different view-points. Then, a classifier based on random forests is trained based on these synthetic images. In the classification stage, we also create synthetic images for each testing image, and the synthetic images are classified with the pre-trained classifier. The final decision for this testing image is made by the majority voting of the classification results of all these synthetic images. The classification performance is evaluated on the UIUC texture dataset. Our method achieves the classification rate of 99.21%, which is higher than most of the state-of-the-arts.

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“…They have been implictely used to characterize the LOIDs by popular approaches such as local binary patterns (LBP), maximum response of oriented filterbanks, and the scale-invariant feature transform (SIFT). LBPs [5] and their extensions [22][23][24][25][26][27][28][29] are specifically encoding the LOIDs in a rotation-invariant fashion with uniform circular pixel sequences. Extensions were proposed to include richer pixel dependencies based on local differences [22] and medians [29].…”

mentioning

confidence: 99%

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification

Depeursinge

Püspöki

Ward

et al. 2017

IEEE Trans. on Image Process.

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We present texture operators encoding class-specific local organizations of image directions (LOIDs) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches, such as local binary patterns (LBPs) and the scale-invariant feature transform (SIFT). Whereas, LBPs and SIFT yield hand-crafted image representations, we propose to learn data-specific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHWs), offering a rich and yet compact initial representation for characterizing natural textures. The joint location and orientation required to encode the LOIDs is preserved by using moving frames (MFs) texture representations built from locally-steered image gradients that are invariant to rigid motions. In a second step, we use support vector machines to learn a multi-class shaping matrix for the initial CHW representation, yielding data-driven MFs called steerable wavelet machines (SWMs). The SWM forward function is composed of linear operations (i.e., convolution and weighted combinations) interleaved with non-linear steermax operations. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures. Our scheme outperforms recent approaches on several test suites of the Outex and the CUReT databases.

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Noise-invariant structure pattern for image texture classification and retrieval

Cited by 10 publications

References 19 publications

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Multi-view texture classification using hierarchical synthetic images

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification

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