Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

Deep Learning for Hyperspectral Image Classification: An Overview

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .

An Underwater Image Enhancement Benchmark Dataset and Beyond

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.

Going Deeper With Contextual CNN for Hyperspectral Image Classification

Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness module is introduced to learn the affinity matrix via a typical regularization term (e.g., sparse or low-rank). However, the adopted regularization terms ignore the connectivity within each subspace, limiting their clustering performance. In addition, the adopted framework suffers from the coupling issue between the auto-encoder module and the self-expressiveness module, making the network training non-trivial. To tackle these two issues, we propose a novel deep subspace clustering method named Maximum Entropy Subspace Clustering Network (MESC-Net). Specifically, MESC-Net maximizes the entropy of the affinity matrix to promote the connectivity within each subspace, in which its elements corresponding to the same subspace are uniformly and densely distributed. Meanwhile, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module. Besides, we also theoretically prove that the learned affinity matrix satisfies the block-diagonal property under the assumption of independent subspaces. Extensive quantitative and qualitative results on commonly used benchmark datasets validate MESC-Net significantly outperforms state-of-the-art methods. The code is publicly available at.

Maximum Entropy Subspace Clustering Network

Rate-distortion optimal 3D point cloud compression is very challenging due to the irregular structure of 3D point clouds For a popular 3D point cloud codec that uses octrees for geometry compression and JPEG for color compression, we first find analytical models that describe the relationship between the encoding parameters and the bitrate and distortion, respectively We then use our models to formulate the rate-distortion optimization problem as a constrained convex optimization problem and apply an interior point method to solve it Experimental results for six 3D point clouds show that our technique gives similar results to exhaustive search at only about 157% of its computational cost

Model-Based Encoding Parameter Optimization for 3D Point Cloud Compression

Depth estimation is a fundamental problem for light field based applications. Although recent learning-based methods have proven to be effective for light field depth estimation, they still have troubles when handling occlusion regions. In this paper, by leveraging the explicitly learned occlusion map, we propose an occlusion-aware network, which is capable of estimating accurate depth maps with sharp edges. Our main idea is to separate the depth estimation on non-occlusion and occlusion regions, as they contain different properties with respect to the light field structure, i.e., obeying and violating the angular photo consistency constraint. To this end, three modules are involved in our network: the occlusion region detection network (ORDNet), the coarse depth estimation network (CDENet), and the refined depth estimation network (RDENet). Specifically, ORDNet predicts the occlusion map as a mask, while under the guidance of the resulting occlusion map, CDENet and REDNet focus on the depth estimation on non-occlusion and occlusion areas, respectively. Experimental results show that our method achieves better performance on 4D light field benchmark, especially in occlusion regions, when compared with current state-of-the-art light-field depth estimation algorithms.

Accurate Light Field Depth Estimation via an Occlusion-Aware Network

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin.

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

Pairwise constraint propagation (PCP) aims to propagate a limited number of initial pairwise constraints (PCs, including must-link and cannot-link constraints) from the constrained data samples to the unconstrained ones to boost subsequent PC-based applications. The existing PCP approaches always suffer from the imbalance characteristic of PCs, which limits their performance significantly. To this end, we propose a novel imbalance-aware PCP method, by comprehensively and theoretically exploring the intrinsic structures of the underlying PCs. Specifically, different from the existing methods that adopt a single representation, we propose to use two separate carriers to represent the two types of links. And the propagation is driven by the structure embedded in data samples and the regularization of the local, global, and complementary structures of the two carries. Our method is elegantly cast as a well-posed constrained optimization model, which can be efficiently solved. Experimental results demonstrate that the proposed PCP method is capable of generating more high-fidelity PCs than the recent PCP algorithms. In addition, the augmented PCs by our method produce higher accuracy than state-of-the-art semi-supervised clustering methods when applied to constrained clustering. To the best of our knowledge, this is the first PCP method taking the imbalance property of PCs into account.

Junhui Hou

Papers

Maximum Entropy Subspace Clustering Network

Model-Based Encoding Parameter Optimization for 3D Point Cloud Compression

Accurate Light Field Depth Estimation via an Occlusion-Aware Network

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

Imbalance-aware Pairwise Constraint Propagation