Texture Classification Based on BIMF Monogenic Signals

Pan, Jianjia; Tang, Yuan Yan

doi:10.1007/978-3-642-37444-9_14

Cited by 6 publications

(8 citation statements)

References 24 publications

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“…Riesz transform based image analysis has been utilized in numerous fields [16,24,32,35], but prior to applying Riesz transform to image analysis, it is necessary to pre-filter the image with a suitable band-pass filter, because real images commonly contain a wide range of frequencies. So far, many candidates on the band-pass filter have been proposed in the literature.…”

Section: Higher-order Riesz Transformmentioning

confidence: 99%

“…Recently, the combination of a Riesz-based method and LBP has been shown to be an effective way for many applications [16,24,33]. Moreover, Lei et al [19] developed an approach to exploit the neighbouring relationship in the spatial domain and various Gabor faces.…”

Section: Riesz-based Volume Local Binary Patterns (Rvlbp)mentioning

confidence: 99%

“…Recent studies [9,16,24,33,35,36] have demonstrated the local image information can be well characterized in a unified theoretic framework, namely the Riesz transform. Felsberg and Sommer proposed to use the Riesz transform for image processing [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, from the intrinsic dimension theorem [34], the 1st-order Riesz transform is designed just for an intrinsic 1D signal, but losing some important complex structures, such as corners. In order to characterize the intrinsic 2D local structures, the higher-order Riesz transforms have been developed for biometric recognition [35] and texture recognition [24]. Zhang et al [35] proposed to utilize the 1st-order and 2nd-order Riesz transforms to encode the local patterns of biometric images.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it begs the question if the higher-order Riesz transforms can also provide rich information for face representation. Motivated by [24,35], we propose a new method based on the higher-order Riesz transform and local binary patterns for characterizing facial expressions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Huang¹,

Dhall²,

Zhao³

et al. 2015

Procedings of the British Machine Vision Conference 2015

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Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.

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Section: Higher-order Riesz Transformmentioning

confidence: 99%

Section: Riesz-based Volume Local Binary Patterns (Rvlbp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Huang¹,

Dhall²,

Zhao³

et al. 2015

Procedings of the British Machine Vision Conference 2015

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Texture Classification Using Dense Micro-block Difference (DMD)

Mehta

Егиазарян

2015

Computer Vision -- ACCV 2014

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Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Yuan

Shi

Xia

et al. 2018

IET Computer Vision

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It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit-sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1-1 bitwise matching number to the 0-0 bitwise matching number and the 1-0 number to the 0-1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi-scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

show abstract

Texture Classification Based on BIMF Monogenic Signals

Cited by 6 publications

References 24 publications

Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Texture Classification Using Dense Micro-block Difference (DMD)

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

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