Invariant Features of Local Texturesa rotation invariant local texture descriptor

Janney, Pranam; Yu, Zhen

doi:10.1109/cvpr.2007.383367

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Future work will also include the use of the steerable pyramid by Simoncelli et al [50] as an alternative to the isotropic multiscale framework used in this work. The proposed method outperforms most filterand wavelet-based approaches using the Outex_TC_00010 test suite [1], [26], [27], [29], [39], [42], [43], [51], where only few methods based on LBPs achieve a performance above 98% [14], [19], [21], [24], [25], [30], [33], sometimes with manual parameter optimization. MR8, LM and S filterbanks were reported to obtain performances of 72.57%, 51.8% and 68.61% when combined with SVMs in [16] in 2012 using the same database.…”

Section: Discussionmentioning

confidence: 99%

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Rotation–Covariant Texture Learning Using Steerable Riesz Wavelets

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

IEEE Trans. on Image Process.

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Abstract-We propose a texture learning approach that exploits local organizations of scales and directions. First, linear combinations of Riesz wavelets are learned using kernel support vector machines. The resulting texture signatures are modeling optimal class-wise discriminatory properties. The visualization of the obtained signatures allows verifying the visual relevance of the learned concepts. Second, the local orientations of the signatures are optimized to maximize their responses, which is carried out analytically and can still be expressed as a linear combination of the initial steerable Riesz templates. The global process is iteratively repeated to obtain final rotationcovariant texture signatures. Rapid convergence of class-wise signatures is observed, which demonstrates that the instances are projected into a feature space that leverages the local organizations of scales and directions. Experimental evaluation reveals average classification accuracies in the range of 97% to 98% for the Outex_TC_00010, the Outex_TC_00012, and the Contrib_TC_00000 suites for even orders of the Riesz transform, and suggests high robustness to changes in images orientation and illumination. The proposed framework requires no arbitrary choices of scales and directions and is expected to perform well in a large range of computer vision applications.

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Section: Discussionmentioning

confidence: 99%

“…It has recently been used by several studies on texture recognition [14], [16], [19], [21], [24]- [27], [29], [30], [33], [39], [42], [51]. It consists of 24 texture classes with pronounced directional structures.…”

Section: F Data Setsmentioning

confidence: 99%

Rotation–Covariant Texture Learning Using Steerable Riesz Wavelets

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

IEEE Trans. on Image Process.

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“…This database has been used with different levels of complexity in texture classification [18], texture segmentation [19], and image retrieval [20]. A rotation invariant version of the Brodatz database was also proposed [21] and used for texture classification and retrieval [22,23].…”

Section: Introductionmentioning

confidence: 99%

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Safia

2013

ISRN Machine Vision

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Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

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Framework for Illumination Invariant Vehicular Traffic Density Estimation

Janney

Geers

2009

Advances in Image and Video Technology

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Intelligent machines require basic information such as moving-object detection from videos in order to deduce higher-level semantic information. In this paper, we propose a methodology that uses a texture measure to detect moving objects in video. The methodology is computationally inexpensive, requires minimal parameter finetuning and also is resilient to noise, illumination changes, dynamic background and low frame rate. Experimental results show that performance of the proposed approach is higher than those of state-of-the-art approaches. We also present a framework for vehicular traffic density estimation using the foreground object detection technique and present a comparison between the foreground object detection-based framework and the classical density state modelling-based framework for vehicular traffic density estimation.

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Invariant Features of Local Texturesa rotation invariant local texture descriptor

Cited by 7 publications

References 11 publications

Rotation–Covariant Texture Learning Using Steerable Riesz Wavelets

Rotation–Covariant Texture Learning Using Steerable Riesz Wavelets

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Framework for Illumination Invariant Vehicular Traffic Density Estimation

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