Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch labels are poorly assigned. In this paper, we tackle this problem by a novel way to update the RF codebook learning for a more discriminative codebook with the introduction of the soft class labels, estimated from the pLSA model based on a feedback scheme. The feedback scheme is performed on both the image and patch levels respectively, which is in contrast to the state- of-the-art RF codebook learning that focused on either image or patch level only. Experiments on 15-Scene and C-Pascal datasets had shown the effectiveness of the proposed method in image understanding task.
Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely regression-enhanced random forests (RERFs), that can improve on RFs by borrowing the strength of penalized parametric regression. The algorithm for constructing RERFs and selecting its tuning parameters is described. Both simulation study and real data examples show that RERFs have better predictive performance than RFs in important situations often encountered in p…
Amity University（阿米提大学）；Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）
Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dat…
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpr…
Sharif University of Technology（谢里夫理工大学）；Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty…
In this paper, we introduce a collaborative training algorithm of balanced random forests with convolutional neural networks for domain adaptation tasks. In real scenarios, most domain adaptation algorithms face the challenges from noisy, insufficient training data and open set categorization. In such cases, conventional methods suffer from overfitting and fail to successfully transfer the knowledge of the source to the target domain. To address these issues, the following two techniques are proposed. First, we introduce the optimized decision tree construction method with convolutional neural…
deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection
Chair of Sensor-based Geoinformatics, University of Freiburg（传感器基于地理信息学系，弗赖堡大学）；EcoVision Lab, DM3L, University of Zurich（生态视觉实验室，苏黎世大学）；Institute for Earth System Science and Remote Sensing, Leipzig University（地球系统科学与遥感研究所，莱比锡大学）；Washington University, St. Louis（斯蒂芬斯敦大学）
Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has shown high imputation accuracy under the Missing (Completely) at Random scheme with various missing rates. In its core, it is based on a random forest for classification and regression, respectively. In this paper we study whether this approach can even be enhanced by other methods s…
Vencera Consulting（Vencera咨询公司）；Massachusetts Institute of Technology（麻省理工学院）；Southern University of Science and Technology（南方科技大学）
Conventionally, random forests are built from "greedy" decision trees which each consider only one split at a time during their construction. The sub-optimality of greedy implementation has been well-known, yet mainstream adoption of more sophisticated tree building algorithms has been lacking. We examine under what circumstances an implementation of less greedy decision trees actually yields outperformance. To this end, a "stepwise lookahead" variation of the random forest algorithm is presented for its ability to better uncover binary feature interdependencies. In contrast to the greedy appr…
Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Xidian University（西安电子科技大学）；Wake Forest University（维克森林大学）；University of California, Berkeley（加州大学伯克利分校）；Tulane University（杜兰大学）；Lingnan University（岭南大学）
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）
Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predic…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to th…
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
The Hong Kong University of Science and Technology（香港科技大学）；Southern Medical University（南方医科大学）；Guangdong Provincial Key Laboratory of Molecular Tumor Pathology（广东省分子肿瘤病理学重点实验室）；Jinfeng Laboratory（金凤实验室）；Shandong First Medical University（山东第一医科大学）；Shandong Provincial Qianfoshan Hospital（山东省千佛山医院）；The Third Affiliated Hospital of Kunming Medical University（昆明医科大学第三附属医院）；Yunnan Cancer Hospital（云南省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）；Prince of Wales Hospital（威尔士亲王医院）；Sun Yat-sen University（中山大学）
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating occluded and cluttered objects' 6D pose given a candidate 2D bounding box. Our Iterative Hough Forest is learnt using patches extracted only from the positive samples. These patches are represented with Histogram of Control Points (HoCP), a "scale-variant" implicit volumetric description, which we …
University of Minnesota（明尼苏达大学）；University of Minnesota Informatics Institute（明尼苏达大学信息学研究所）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）
Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar …
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统管理与控制国家重点实验室）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；Nanchang University（南昌大学）
Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian-Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In c…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Kentucky Imaging and Technologies（肯塔基成像技术公司）
Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas cl…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；School of Computer and Control Engineering, Yantai University（烟台大学计算机与控制工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of th…
Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；SenseTime Research（商汤科技研究院）
Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程研究所）；Concordia University（康考迪亚大学）；Université TELUQ（TELUQ大学）
Site preparation by mounding is a commonly used silvicultural treatment that improves tree growth conditions by mechanically creating planting microsites called mounds. Following site preparation, the next critical step is to count the number of mounds, which provides forest managers with a precise estimate of the number of seedlings required for a given plantation block. Counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas. To address this issue, we present a novel framework explo…
Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring
Renmin University of China（中国人民大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；International Research Institute of Disaster Science (IRIDeS), Tohoku University（东北大学灾害科学国际研究所）
Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training …
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Zhongshan Hospital, Fudan University（复旦大学中山医院）
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Centre for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo（模式分析与机器智能中心，电气与计算机工程系，滑铁库大学）；Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence（人工智能穆罕默德·本·扎耶德大学机器学习系）
WaveInst: A Frequency-Domain Enhanced Network for Fine-Grained Thin Tree Trunk Extraction in Forest Scenes
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学与人工智能学院）；Department of Geography, College of Natural Resources and Environment, Virginia Tech（维吉尼亚理工大学地理系）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）；State Forestry and Grassland Administration, Key Laboratory of Forest Management and Growth Modelling（国家林业和草原局森林管理与生长建模重点实验室）；School of Geospatial Artificial Intelligence, East China Normal University（华东师范大学地理空间人工智能学院）
HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement in Game Engines
organization= School of Informatics , addressline= Aristotle University of Thessaloniki (AUTH) , city= Thessaloniki , postcode= 54124 , country= Greece
Halmstad University（哈尔姆斯塔德大学）；University of Malta（马耳他大学）
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We v…
Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relatio…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
In this paper, we focus on estimating the 6D pose of objects in point clouds. Although the topic has been widely studied, pose estimation in point clouds remains a challenging problem due to the noise and occlusion. To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses. 3DPVNet is comprised of three modules. In particular, a Patch Unification (\textbf{PU}) module is first introduced to normalize the input patch, and also create a standard local coordinate frame on it to generate a reliable vote. We then devise a Weigh…
Charles Sturt University（查尔斯特大学）；University of Western Sydney（西悉尼大学）；Kent Institute Australia（肯特学院）；Asia Pacific International College（亚太国际学院）；Australian Institute of Higher Education（澳大利亚高等教育学院）；Al Iraqia University（伊拉克大学）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；AL Nisour University College（阿尔尼苏尔大学学院）
Background and Aim: Recently, deep learning using convolutional neural network has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a Patch-Based Classifier (PBC) along with deep learning architecture. Methodology: The proposed system consists of a Deep Convolutional Neural Network (DCNN) that helps in enhancing and increasing the accuracy of the classification proce…
Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained Novelty Detection in Medical Imaging
the Southern University of Science and Technology（南方科技大学）；University of Oxford（牛津大学）；Tsinghua University（清华大学）；University of Warwick（华威大学）；Northwest University（西北大学）；Peng Cheng Lab（鹏城实验室）
