Image recognition via two-dimensional random projection and nearest constrained subspace

Liao, Liang; Zhang, Yanning; Maybank, Stephen J.; Liu, Zhoufeng; Liu, Xin

doi:10.1016/j.jvcir.2014.03.007

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…It is difficult for a monocular imaging system to recover the three-dimensional information of an object; the two-dimensional image acquired by the binocular imaging system from two angles can effectively recover the original three-dimensional information of the object [12]. Therefore, this study is based on binocular imaging.…”

Section: Methodsmentioning

confidence: 99%

Foreign object recognition technology for port transportation channel based on automatic image recognition

Jiang

Peng

et al. 2018

J Image Video Proc.

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In the port transportation, the transportation vehicle is extremely easy to cause a bad condition in the transportation process due to the influence of foreign matter, and the current foreign matter detection is still carried out by manual methods. Based on this, this study is based on the automatic image recognition technology, using the binocular imaging system for video collection and analyzing the video images for foreign object recognition. We use extreme median filtering to perform image noise processing and improve the traditional Canny edge detection algorithm to obtain an improved edge detection method for Canny operator. The algorithm is used to perform image edge detection, and the image gray histogram is used to enhance the foreign object image processing. Combined with the experimental analysis, the detection method of this study has certain effects, which can be used for foreign object identification in port transportation channels and can provide theoretical reference for subsequent related research.

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Section: Methodsmentioning

confidence: 99%

Foreign object recognition technology for port transportation channel based on automatic image recognition

Jiang

Peng

et al. 2018

J Image Video Proc.

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“…Equation 15 is a typical problem of combination optimization. To avoid such a N-P hard problem, we offer a greedy algorithm called COMP (Constrained Orthogonal Matching Pursuit) for approximately solving for ® ¤ in equation 15.…”

Section: B Global Ncsc and Compmentioning

confidence: 99%

“…In our previous works [14], [15], we have proposed a generalized classifier framework called NCSC (Nearest Constrained Subspace Classifier), which classifies a vector y as follows.…”

mentioning

confidence: 99%

Connecting the Dots: Image Classification via Sparse Representation from a Constrained Subspace Perspective

Liao

Maybank²,

Ye³

et al. 2018

Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

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We consider the problem of classifier design via sparse representation based on a constrained subspace model. We argue that the data points in the linear span of the training samples should be constrained in order to yield a more accurate approximation to the corresponding data manifold. For this purpose, the constrained set of data points is formulated as a union of affine subspaces in the form of affine hulls spanned by training samples. We argue that the intrinsic dimension of the affine subspaces should be equal to that of data manifold. Thus, a classifier based on this model has a high classification accuracy similar to that of the conceptual NM (Nearest Manifold) classifier. Based on this model, we connect the dots of some classical classifiers including NN (Nearest Neighbor), NFL (Nearest Feature Line), NS (Nearest subspace) and the recently emerged state-of-the-art SRC (Sparse Representation Classifiers) and interpret the mechanism of SRC and Yang's variant of the SRC using the constrained subspace perspective. Experiments on the Extended Yale B database for image classification corroborate our claims and demonstrate the possibility of a proposed classifier called NCSC-CSR which has higher classification accuracy and robustness.

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Fast indoor scene description for blind people with multiresolution random projections

Mekhalfi

Melgani

Bazi

et al. 2017

Journal of Visual Communication and Image Representation

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Image recognition via two-dimensional random projection and nearest constrained subspace

Cited by 7 publications

References 60 publications

Foreign object recognition technology for port transportation channel based on automatic image recognition

Foreign object recognition technology for port transportation channel based on automatic image recognition

Connecting the Dots: Image Classification via Sparse Representation from a Constrained Subspace Perspective

Fast indoor scene description for blind people with multiresolution random projections

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