Character Recognition Based on PCANet

Liu, Renjun; Lu, Tongwei

doi:10.1109/ispdc.2016.60

Cited by 4 publications

(5 citation statements)

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“…p, , ∈ (1) Only in this way, can we make pixel vector p became a square, its border length and width is n. Suppose that N input training sample images of size isn × n are given, and the patch size is k × k . First of all ,the local features are selected for each training image by using the patch, then subtract patch mean from each and obtain [3] : = ̅ , , ̅ , , … , ̅ , (2) Where, x , is a mean-removed patch, Constructing the same matrix for all input images and putting them together to get: = , , … , ∈ ℝ , ×…”

Section: Main Stepsmentioning

confidence: 99%

“…The second stage of PCA process is similar with the first, Just it is noting that the second stage input is output of the first. Assume that l th filter output of first stage be [3] :…”

Section: The Second Stage Of Pcamentioning

confidence: 99%

“…Then we view the vector of binary bit as a decimal number, so that an output matrix can converst into a single integer image. The calculation formula is as follows [3] :…”

Section: Hash Operationmentioning

confidence: 99%

“…PCANet is proposed in [2],mainly using Face Recognition and Handwritten digit recognition, and has made great success in printed document recognition, and it is a kind of deep learning framework by cascading PCA as in convolution neural network. Consequently, a great number of classification methods have been proposed in character recognition [3] , especially in scene character recognition [4] , but Hyperspectral images classification using PCANet is only a little in recent 2 years. HSIs classification focuses on extracting effective features, combining with complicated spectral, spatial information ,classifier and so on.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image classification based on multi-layer feature extraction

Yang

2018

MATEC Web Conf.

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The Hyperspectral image classification is an important issue, which has been pursued in recent year. The field of application involves many aspects of life. Hyperspectral images (HSIs) exhibit a limited number of labeled high-dimensional training samples, which limits the performance of some classification methods on feature extraction or feature reduction. In the paper, we propose a supervised method based on the PCA network (PCANet) and linear SVM for HSIs classification. We used PCANet (principal component analysis network) to learn the character features. We verified the influence of these parameters on the performance of PCANet by modifying the key parameters of the experiment. We carry out extensive experiments on India pines dataset. The results demonstrate that our method significantly outperforms PCA+KNN methods . And the results not only are optimistic but also the recognition rate can reach 94.29%. At last, we compared the experimental results of the same algorithm on different data sets and so on.

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Section: Main Stepsmentioning

confidence: 99%

“…The second stage of PCA process is similar with the first, Just it is noting that the second stage input is output of the first. Assume that l th filter output of first stage be [3] :…”

Section: The Second Stage Of Pcamentioning

confidence: 99%

“…Then we view the vector of binary bit as a decimal number, so that an output matrix can converst into a single integer image. The calculation formula is as follows [3] :…”

Section: Hash Operationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image classification based on multi-layer feature extraction

Yang

2018

MATEC Web Conf.

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“…Since it can achieve an excellent classification performance and its structure is simple as well as it is easy to perform the optimization of the hyper-parameters [2], the PCANET is useful for performing the image classification, the target recognition and other processing. As a result, many classification applications such as the character recognition [10] and the scene character recognition [11] have been developed. However, the PCANET is rarely used for performing the HSI classification.…”

Section: Introductionmentioning

confidence: 99%

Regional Principal Component Analysis Network With the Rolling Guidance Filter for Classifying the Hyperspectral Images

et al. 2020

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Because conventional PCANET approach is that the conventional PCANET performs the PCA for all the segments of all the training pixel vectors, and this does not capture the difference between different segments of the same training pixel vectors, classification accuracy is not high. This paper proposes to employ a regional principal component analysis network with the rolling guidance filter (RPCANET_RGF) for performing the hyperspectral image (HSI) classification with few training samples. Regional principal component analysis network (RPCANET) proposed in this paper performs the PCA for each segment of all training pixel vectors. Besides, the rolling guidance filter (RGF) is used to remove the spatial noise and to enhance the edges of the HSIs. Different from the conventional convolutional neural networks (CNNs), the coefficients of the filters are obtained by performing the principal component analysis (PCA) on the regional segments of HSIs. This approach is also different from the conventional principal component analysis network (PCANET). Here, different segments of the same pixel image are processed by different Filters. Since the RPCANET_RGF is a general learning method that obtains the filter coefficients directly from the HSIs, the back propagation based training is not required. Hence, the RPCANET_RGF requires a less computational power for performing the training compared to the CNN. Besides, as the RPCANET_RGF can make use of both the spectral information and the spatial information for performing the classification, the computer numerical simulation results show that the classification accuracy achieved by the RPCANET_RGF is higher than that by the conventional PCANET and other state of the art methods. INDEX TERMS Regional principal component analysis network, rolling guidance filter, hyperspectral image, classification.

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