Mixed attention dense network for sketch classification

Applied Intelligence - State-of-the-art convolutional neural networks (CNNs) on sketch classification cannot balance the expression ability of final feature vectors and the problems of gradient...     

Spatially adaptive multi-scale contextual attention for image inpainting

Multimedia Tools and Applications - Image inpainting is the task to fill missing regions of an image. Recently, researchers have achieved a great performance by using convolutional neural networks...     

Deep mixed precision for hyperspectral image classification

The Journal of Supercomputing - Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this...     

融合注意力机制的多阶段舌象分类算法

为解决舌象分类算法容易受到面部无关信息以及舌部杂质信息的干扰,造成分类准确率下降的问题,设计一种融合注意力机制的多阶段舌象分类算法.通过舌部定位阶段提取不同感受视野的舌象特征进行融合,获得舌部区域,减轻面部信息干扰;在舌象分类阶段基于舌部区域,借助注意力机制模块抑制舌部杂质信息的干扰,提取精准特征,进行分类.将算法得到...     

Lightweight multi-scale aggregated residual attention networks for image super-resolution

Multimedia Tools and Applications - Recently, single image super-resolution (SISR) based on convolutional neural networks (CNNs) has represented great progress. However, due to the huge number of...     

Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification

Hyperspectral image (HSI) classification is a very active research topic in remote sensing and has numerous potential applications. This paper presents a simple but effective classification method based on spectral-spatial information and K-nearest neighbor (KNN). To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all neighboring pixels of a given test pixel and training samples. In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By using two standard HSI benchmark, we evaluate the proposed method by comparing it with eight competing methods. Both qualitative and quantitative results demonstrate our SSKNN method achieves better performance than other ones.     

Multi-stack hybrid CNN with non-monotonic activation functions for hyperspectral satellite image classification

Neural Computing and Applications - The application of deep learning techniques in hyperspectral satellite images (HSI) classification has led to a significant increase in accuracy compared to...     

Spatial-spectral locality preserving projection for hyperspectral image classification with limited training samples

Locality preserving projection (LPP) is a popular unsupervised feature extraction (FE) method. In this paper, the spatial-spectral LPP (SSLPP) method is proposed, which uses both the spectral and spatial information of hyperspectral image (HSI) for FE. The proposed method consists of two parts. In the first part, unlabelled samples are selected in a spatially homogeneous neighbourhood from filtered HSI. In the second part, the transformation matrix is calculated by an LPP-based method and by using the spectral and spatial information of the selected unlabelled samples. Experimental results on Indian Pines (IP), Kennedy Space Center (KSC), and Pavia University (PU) datasets show that the performance of SSLPP is superior to spectral unsupervised, supervised, and semi-supervised FE methods in small and large sample size situations. Moreover, the proposed method outperforms other spatial-spectral semi-supervised FE methods for PU dataset, which has high spatial resolution. For IP and KSC datasets, spectral regularized local discriminant embedding (SSRLDE) has the best performance by using spectral and spatial information of labelled and unlabelled samples, and SSLPP is ranked just behind it. Experiments show that SSLPP is an efficient unsupervised FE method, which does not use training samples as preparation of them is so difficult, costly, and sometimes impractical. SSLPP results are much better than LPP. Also, it decreases the storage and calculation costs using less number of unlabelled samples.     

DCNR: deep cube CNN with random forest for hyperspectral image classification

Multimedia Tools and Applications - Hyperspectral Image (HSI) classification is one of the fundamental tasks in the field of remote sensing data analysis. CNN (Convolutional Neural Network) has...     

Learning multi-level and multi-scale deep representations for privacy image classification

Multimedia Tools and Applications - 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...     

基于组合分类器的遥感图像多尺度分类研究

遥感图像（RSI）的特殊性使得图像的准确分类变得非常困难。提出了一种自适应多尺度分割的组合分类算法。采用组合分类的办法,也就是将一组功能较弱的分类器联合起来构成一个功能较强的分类器。每一个较弱的分类器都由一级分割来训练并且描述。较弱的分类器可以由线性支持向量机（SVM）和区域距离构成。实验表明该方法能够准确地实现图像的分类并且与实际图像相符。此外,采用分级的多尺度分析方法能够减少训练时间,得到一个性能更好的分类器。仿真表明该方法比其他方法性能更优。     

Spatial-spectral neighbour graph for dimensionality reduction of hyperspectral image classification

Recently, graph embedding-based methods have drawn increasing attention for dimensionality reduction (DR) of hyperspectral image (HSI) classification. Graph construction is a critical step for those DR methods. Pairwise similarity graph is generally employed to reflect the geometric structure in the original data. However, it ignores the similarity of neighbouring pixels. In order to further improve the classification performance, both spectral and spatial-contextual information should be taken into account in HSI classification. In this paper, a novel spatial-spectral neighbour graph (SSNG) is proposed for DR of HSI classification, which consists of the following four steps. First, a superpixel-based segmentation algorithm is adopted to divide HSI into many superpixels. Second, a novel distance metric is utilized to reflect the similarity of two spectral pixels in each superpixel. In the third step, a spatial-spectral neighbour graph is constructed according to the above distance metric. At last, support vector machine with a composite kernel (SVM-CK) is adopted to classify the dimensionality-reduced HSI. Experimental results on three real hyperspectral datasets demonstrate that our method can achieve higher classification accuracy with relatively less consumed time than other graph embedding-based methods.     

Cyclic learning rate based HybridSN model for hyperspectral image classification

Classification of remotely sensed hyperspectral images (HSI) is a challenging task due to the presence of a large number of spectral bands and due to the less available data of remotely sensed HSI. The use of 3D-CNN and 2D-CNN layers to extract spectral and spatial features shows good test results. The recently introduced HybridSN model for the classification of remotely sensed hyperspectral images is the best to date compared to the other state-of-the-art models. But the test performance of the HybridSN model decreases significantly with the decrease in training data or number of training epochs. In this paper, we have considered cyclic learning for training of the HybridSN model, which shows a significant increase in the test performance of the HybridSN model with 10%, 20%, and 30% training data and limited number of training epochs. Further, we introduce a new cyclic function (ncf) whose training and test performance is comparable to the existing cyclic learning rate policies. More precisely, the proposed HybridSN(ncf ) model has higher average accuracy compared to HybridSN model by 19.47%, 1.81% and 8.33% for Indian Pines, Salinas Scene and University of Pavia datasets respectively in case of 10% training data and limited number of training epochs.

     

MSANet: Multi-scale attention networks for image classification

Multimedia Tools and Applications - The classification of images based on the principles of human vision is a major task in the field of computer vision. It is a common method to use multi-scale...     

Fast stripe noise removal from hyperspectral image via multi-scale dilated unidirectional convolution

Multimedia Tools and Applications - Hyperspectral images (HSIs) are often contaminated by noises due to the multi-detector imaging systems, which greatly affects the subsequent HSIs interpretation...     

SSNET: an improved deep hybrid network for hyperspectral image classification

Neural Computing and Applications - Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been...     

A discriminant sparse representation graph-based semi-supervised learning for hyperspectral image classification

Multimedia Tools and Applications - The classification of hyperspectral image with a paucity of labeled samples is a challenging task. In this paper, we present a discriminant sparse representation...     

An ensemble multi-scale residual attention network (EMRA-net) for image Dehazing

Multimedia Tools and Applications - Image dehazing aims to recover a clean image from a hazy image, which is a challengingly longstanding problem. In this paper, we propose an Ensemble Multi-scale...     

Scale fusion light CNN for hyperspectral face recognition with knowledge distillation and attention mechanism

Applied Intelligence - Hyperspectral imaging technology, combining traditional imaging and spectroscopy technologies to simultaneously acquire spatial and spectral information, is deemed to be an...