New color GPHOG descriptors for object and scene image classification

Sinha, Atreyee; Banerji, Sugata; Liu, Chengjun

doi:10.1007/s00138-013-0561-6

Cited by 24 publications

(15 citation statements)

References 38 publications

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“…The most of the color spaces have been developed for specific applications, but all come from the same concept: the trichromatic theory of primary colors of R, G and B [40]. Other color spaces are usually calculated from the RGB color space via either linear or nonlinear transformations [33][34][35][36]41]. However, RGB is not very efficient when dealing with "real-world" images.…”

Section: Rgb Color Spacementioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Fan

Yang

et al. 2017

Preprint

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Abstract:In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

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Section: Rgb Color Spacementioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Fan

Yang

et al. 2017

Preprint

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“…In this section, we make a comparative evaluation of our HLG SURF descriptor with other state of the art methods (Banerji et al [2] and Sinha et al [17]) in image classification using the MIT scene dataset. In this section, we apply two different codebook sizes (256 and 1024) to evaluate our method.…”

Section: Image Classification On Mit Scene Datasetmentioning

confidence: 99%

“…Banerji et al [2] presented a CGLF+PHOG descriptor by concatenating the colour greyscale LBP fusion (CGLF) and the pyramid of histograms of orientation gradients (PHOG) for scene and object image classification. Sinha et al [17] proposed a fused colour Gabor-pyramid of histograms of oriented gradients (FC-GPHOG) descriptor for object and scene image classification. Yu et al [1] proposed a CBIR method based on bag-of-features model by fusing LBP and SIFT algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hybrid local and global descriptor enhanced with colour information

Kabbai

Abdellaoui

Douik

2017

IET Image Processing

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“…Several texture analysis approaches based on global feature, include color Gabor filtering [6], Markov random field model [7]. Some of the effective local feature methods are color scale invariant feature transform (SIFT) [8], color pyramid of histograms of [9], discriminative color descriptors (DCD) [10], three-dimensional adaptive sum and difference histograms (3D-ASDH) [11], color local binary pattern [12,13] and affine wavelet [14]. Among of them, histogram of oriented gradients (HOG) [15] is successfully applied for image classification and object detection.…”

Section: Introductionmentioning

confidence: 99%

Rice seed image classiﬁcation based on HOG descriptor with missing values imputation

Quoc

Hoang

2020

TELKOMNIKA

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Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.

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New color GPHOG descriptors for object and scene image classification

Cited by 24 publications

References 38 publications

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Hybrid local and global descriptor enhanced with colour information

Rice seed image classiﬁcation based on HOG descriptor with missing values imputation

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