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Privacy image classification can help people detect privacy images when people share images. In this paper, we propose a novel method using multi-level and multi-scale features for privacy image classification. We first use CNN (Convolutional Neural Network) to extract multi-levels features. Then, max-pooling layers are employed to obtain multi-scale features at each level. Finally, we propose two feature aggregation models, called Privacy-MSML and Privacy-MLMS to fuse those features for image privacy classification. In Privacy-MSML, multi-scale features of the same level are first integrated and then the integrated features are fused. In Privacy-MLMS, multi-level features of the same scale are first integrated and then the integrated features are fused. Our experiments on a real-world dataset demonstrate the proposed method can achieve better performance compared with the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Abstract Histopathology image analysis is a gold standard for cancer recognition and diagnosis. But typical problems with histopathology images that hamper automatic analysis include complex clinical features, insufficient training data, and large size of a single image (always up to gigapixels). In this paper, an image semantic segmentation algorithm based on feature Pyramid (ResNet50-GICN-GPP) is proposed. Firstly, the patch sampling method is adopted to resample the image, reduce the size of a single sample, and expand the number of training samples. Secondly, design the whole convolution network based on ResNet50 learning feature location information, then use GICN structure and deconvolution network to integrate multi-level features. Finally, in order to solve the problem that the GICN structure may lose the small object, the GPP structure should be joined to explore the multi-scale semantic information. The proposed method achieves 63% of the average segmentation accuracy (Dice coefficient) on Camelyon16 and Gastric WSIs Data, compared with U-Net, FCN and SegNet which has 10~20% improvement, and fully demonstrates the effectiveness of this method in different types of cancer. By experimentally comparing the segmentation accuracy of various scales of cancerous tissues, the performance of ResNet50-GICN-GPP is balanced and the multi-scale information localization is more accurate.

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]