Scale- and rotation-invariant texture description with improved local binary pattern features

Davarzani, Reza; Mozaffari, Saeed; Yaghmaie, Khashayar

doi:10.1016/j.sigpro.2014.11.005

Cited by 39 publications

(13 citation statements)

References 33 publications

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“…Li et al [88] showed a good discrimination capability of LBP sri su2 r,p for texture classification. Davarzani et al [90] proposed Weighted Rotation and Scale Invariant LBP (WRSI LBP) to address rotation and scale variations in texture classification. To achieve rotation invariance, dominant orientation needs to be estimated for each pixel in a image.…”

Section: Preprocessingmentioning

confidence: 99%

“…To achieve scale invariance, an approach similar to the one used in LBP sri su2 r,p [88] is used. Furthermore, Davarzani et al [90] used the minimum magnitude of local differences as an adaptive weight to adjust the contribution of an LBP code in histogram calculation, resulting WLBP operator. One downside of LBP sri su2 r,p and WRSI LBP is that characteristic scale and dominant orientation estimation is computationally expensive and unreliable.…”

Section: Preprocessingmentioning

confidence: 99%

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Local binary features for texture classification: Taxonomy and experimental study

Liu

Fieguth

Guo

et al. 2017

Pattern Recognition

327

175

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Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.

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Section: Preprocessingmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Local binary features for texture classification: Taxonomy and experimental study

Liu

Fieguth

Guo

et al. 2017

Pattern Recognition

327

175

View full text Add to dashboard Cite

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“…A high performance PIH scheme is dependent on suitable features. A local binary pattern (LBP) is originally proposed by Ojala et al [18] and is always an effective texture feature extraction method, due to its rotation and scale invariance [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

2018

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Robustness and discriminability are the two most important features of perceptual image hashing (PIH) schemes. In order to achieve a good balance between perceptual robustness and discriminability, a novel PIH algorithm is proposed by combining latent low-rank representation (LLRR) and rotation invariant uniform local binary patterns (RiuLBP). LLRR is first applied on resized original images to the principal feature matrix and to the salient feature matrix, since it can automatically extract salient features from corrupted images. Following this, Riulocal bin features are extracted from each non-overlapping block of the principal feature matrix and of the salient feature matrix, respectively. All features are concatenated and scrambled to generate final binary hash code. Experimental results show that the proposed hashing algorithm is robust against many types of distortions and attacks, such as noise addition, low-pass filtering, rotation, scaling, and JPEG compression. It outperforms other local binary patterns (LBP) based image hashing schemes in terms of perceptual robustness and discriminability.

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“…Some of the most recently developed methods for texture analysis and classification include algorithms based on Markov random fields [37], structural characteristics [38], chisquared transformation of local-binary-pattern feature [39], block truncation coding [40], texton encoding [41], adaptive median binary patterns [42], space-frequency co-occurrences [43], extension of local binary patterns with scale and orientation information [44], improved local binary pattern features [45], [46], and the combination of the Gabor and curvelet filters [47]. Regarding texture analysis methods applied to the database derived from the Brodatz album [48], while many methods have been applied to the small subsets of the Brodatz database [49], only a few attempts reported on texture classification with the complete Brodatz database [50]- [53], particularly for real-time image retrieval with large databases [50].…”

Section: Introductionmentioning

confidence: 99%

The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval

Pham

2016

IEEE Trans. on Image Process.

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Abstract-Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features, and the combinations of multiple algorithms; while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism; and therefore can be used as a useful tool for both structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching, and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the UIUC texture database suggest the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval.

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Scale- and rotation-invariant texture description with improved local binary pattern features

Cited by 39 publications

References 33 publications

Local binary features for texture classification: Taxonomy and experimental study

Local binary features for texture classification: Taxonomy and experimental study

Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval

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