Hyperspectral image classification is widely used in agriculture, atmospheric environment and other fields. In recent years, deep learning has achieved remarkable success in hyperspectral image classification. However, supervised deep learning largely depends on training sets with high-quality labels, and obtaining large-scale data with high-quality labels is difficult, expensive and time-consuming. Therefore, in response to the problem of insufficient training samples, this paper proposes a hyperspectral image classification method based on generative adversarial networks and spatially uncertain sample selection strategy. By designing two generation networks composed of Autoencoder, the real spectral bands and spatial patches are input into the generation network to generate spectral and spatial information respectively. In order to extract more discriminative features, the discriminator uses different convolution kernels to fuse features and extract joint spatial spectral features. In addition, this paper adopts spatial uncertainty sample selection strategy, which selects more representative and informative samples for labeling. The network designed in this paper is combined with the sample selection strategy to further improve the recognition ability of the discriminator. Experimental results on three hyperspectral image datasets show that compared with several existing methods, this method is less sensitive to the number of training samples and has higher classification performance in the case of limited training samples.
In recent years, image set classification has attracted the attention of many scholars. It is mainly used in complex real scene classification, such as network short video classification, monitoring record classification, and multi-view camera network classification. Pairwise linear regression classification (PLRC) creatively introduces the unrelated subspace to increase the discriminative information, and it achieves a demonstrated better performance on image set classification. However, in the face of large scale image set classification, PLRC is not competent. One possible reason is that it is affected by too many outliers. In order to solve the problem of large scale image set classification, this paper propose two methods. Specifically, one using sparse subspace clustering to mine discriminative features, the other using the self-expressive function to extract exemplar from one image set, both of two methods using PLRC to finish the final classification task, which is not only conductive to reduce the impact of outliers, but also cuts down the computational burden of PLRC. Extensive experiments on two well-known databases prove that the performance of proposed algorithms is better than that of PLRC and several state-of-the-art classifiers.
With the large number of face recognition devices deployed in real application scenarios, face anti-spoofing has become a hot topic nowadays. Previous methods are mostly based on handcrafted features, while recent methods are mostly based on neural networks. However, both the traditional hand-crafted based method and the deep learning methods are still faced with the problem of insufficient generalization ability. In traditional deep learning methods for classification tasks, the label of samples is often a code of the category name. Recent studies have also shown that besides color and distortion, the depth information of face is also considered as an important clue to distinguish real and fake face. In order to combine prior knowledge of face depth information with deep learning method, it is a way worth exploring to expand the label information by using estimated depth image labels instead of coding labels. In this paper, we proposed an auxiliary supervised method to extend label information by using estimated depth information of face. A SA-UNet model which combined spatial attention modules with classic UNet is proposed to generate the depth estimation image for face anti-spoofing. Moreover, contrast depth loss is introduced to focus on the neighborhood information of the pixels, and a scoring method based on the proportion of non-background area is proposed to do the classification. In order to measure the generalization ability of our method, we choose CASIA FASD dataset, Idiap Replay Attack dataset and OULUNPU dataset for experimental verification. Experimental results show that our method is effective for face anti-spoofing task.
The geographic atrophy (GA) caused by retinal layer atrophy is an important clinical manifestation of age- related macular disease (AMD). Automatic segmentation for GA in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. In this paper, we proposed a multi-loss convolutional neural network for GA automatic segmentation based on patient independent. Firstly, to overcome the shortness of samples in medical image processing, the proposed method augmented the samples with samples reversing. Then the model used multi-path block structure to replace single structure of classical CNN to enrich the diversity of features. And the multi-path block loss, cross entropy, and center loss were adapted to supervise and optimize the network effectively, thus it can force the network to learn more representative features. Finally, two data sets were used to evaluate the performance of the model, it demonstrated that the result has a high overlap ratio, correlation coefficient and low absolute area difference. The average overlap ratios on two data sets are 81.88% and 66.86% respectively.
Retina images are mainly obtained by Spectral Domain-Optical Coherence Tomography (SD-OCT), however, most of the acquired volume data are low-resolution(LR) images with noise, making it hard to quantify diseased tissue based on low quality retinal images. In this paper, we propose a denoising Semi-Coupled Dictionary Learning(SCDL) model to reconstruct the noise image while guaranteeing certain noise robustness. First, we use non-local similarities of retina images to construct constraint term, which is added to the objective function of the proposed model. Then, in order to guarantee the fidelity of reconstructed image, the initialized interpolation section should be replaced by the corresponding LR image after SR reconstruction. However, the noise in LR image will affects the reconstructed image quality. So we perform bilateral filtering on the LR image before replacement. Last, two sets of experiments on retinal noise images validate that our proposed method outperforms other state-of-the-art methods.
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