Semantic Fusion of Image Annotation

2011 ◽  
Vol 268-270 ◽  
pp. 1386-1389
Author(s):  
Xiao Ying Wu ◽  
Yun Juan Liang ◽  
Li Li ◽  
Li Juan Ma
Keyword(s):  
Large Scale ◽  
Image Annotation ◽  
Semantic Annotation ◽  
Probability Model ◽  
Joint Probability ◽  
Training Data ◽  
Data Set ◽  
Other Information ◽  
Image Area ◽  
Representative Image

In this paper, improve the image annotation with semantic meaning, and name the new algorithm for semantic fusion of image annotation, that is a image is given to be labeled, use of training data set, the word set, and a collection of image area and other information to establish the probability model ,estimates the joint probability by word and given image areas.The probability value as the size, combined with keywords relevant table that integrates lexical semantics to extract keywords as the most representative image semantic annotation results. The algorithm can effectively use large-scale training data with rich annotation, so as to achieve better recall and precision than the existing automatic image annotation ,and validate the algorithm in the Corel data set.

SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE113-SE122 ◽  
Author(s):  
Yunzhi Shi ◽  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Seismic Image ◽  
Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

scholarly journals Automatic Gully Detection: Neural Networks and Computer Vision

2020 ◽  
Vol 12 (11) ◽  
pp. 1743
Author(s):  
Artur M. Gafurov ◽  
Oleg P. Yermolayev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Satellite Images ◽  
Gully Erosion ◽  
Training Data ◽  
Russian Plain ◽  
Data Set ◽  
High Resolution Satellite Images

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Automatic Video Annotation of Human Health Care Action via Clustering

2020 ◽  
Vol 10 (10) ◽  
pp. 2512-2521
Keyword(s):  
Health Care ◽  
Human Health ◽  
Large Scale ◽  
Image Annotation ◽  
Spinal Injury ◽  
Health Sector ◽  
Video Annotation ◽  
Adjusted Rand Index ◽  
Crowd Sourcing ◽  
Data Set

Vision-based activity monitoring provides applications that revolutionized the e-health sector. Considering the potential of crowdsourcing data, to develop large scale applications, the researchers are working on consolidating smart hospital with crowd sourcing data. For creating a meaningful pattern from such huge data, a key challenge is that it needs to be annotated. Especially, the annotation of medical images plays an important role in providing pervasive health services. Although, multiple image annotation methods such as manual and semi-supervised exist. However, high cost and computation time remains a major issue. To overcome the abovementioned issues, a methodology is proposed for automatic annotation of images. The proposed approach is based on three tires namely frame extraction, interest point's generation, and clustering. Since the medical imaging lacks an appropriate dataset for our experimentation. Consequently, we have introduced a new dataset of Human Health care Actions (HHA). The data set comprises of videos related to multiple medical emergencies such as allergy reactions, burn, asthma, brain injury, bleeding, poisoning, heart attack, choking and spinal injury. We have also proposed an evaluation model to assess the effectiveness of the proposed methodology. The promising results of the proposed technique indicate the effectiveness of 78% in terms of Adjusted Rand Index. Furthermore, to investigate the effectiveness of the proposed technique, a comparison is made, by training the neural network classifier with annotated labels generated by proposed methodology and other existing techniques such as semi-supervised and manual methods. The overall precision of the proposed methodology is 0.75 (i.e., 75%) and semi-supervised learning is 0.69 (69%).

Data set entity recognition based on distant supervision

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengcheng Li ◽  
Qikai Liu ◽  
Qikai Cheng ◽  
Wei Lu
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Data Augmentation ◽  
Scientific Literature ◽  
Neural Model ◽  
Training Data ◽  
Entity Recognition ◽  
Data Set ◽  
Content Type ◽  
Augmentation Techniques

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

AUTOMATIC IMAGE ANNOTATION BASED ON SEMI-SUPERVISED CLUSTERING AND MEMBERSHIP-BASED CROSS MEDIA RELEVANCE MODEL

2012 ◽  
Vol 26 (06) ◽  
pp. 1255009 ◽  
Author(s):  
MOHAMED MAHER BEN ISMAIL ◽  
OUIEM BCHIR
Keyword(s):  
Clustering Algorithm ◽  
Image Annotation ◽  
Feature Weighting ◽  
Training Data ◽  
Automatic Image Annotation ◽  
Data Set ◽  
Cross Media ◽  
Training Images ◽  
Possibilistic Clustering ◽  
Main Components

In this paper, we propose a system for automatic image annotation that has two main components. The first component consists of a novel semi-supervised possibilistic clustering and feature weighting algorithm based on robust modeling of the generalized Dirichlet (GD) finite mixture. This algorithm is used to group image regions into prototypical region clusters that summarize the training data and can be used as the basis of annotating new test images. The constraints consist of pairs of image regions that should not be included in the same cluster. These constraints are deduced from the irrelevance of all concepts annotating the training images to help in guiding the clustering process. The second component of our system consists of a probabilistic model that relies on the possibilistic membership degrees, generated by the clustering algorithm, to annotate unlabeled images. The proposed system was implemented and tested on a data set that include thousands of images using four-fold cross validation.

scholarly journals Current Trends and Future Directions of Large Scale Image and Video Annotation: Observations From Four Years of BIIGLE 2.0

2021 ◽  
Vol 8 ◽  
Author(s):  
Martin Zurowietz ◽  
Tim W. Nattkemper
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Image Annotation ◽  
Training Data ◽  
Video Annotation ◽  
Computer Assisted ◽  
Marine Science ◽  
Imaging Data ◽  
Video Frames ◽  
Increasing Demand

Marine imaging has evolved from small, narrowly focussed applications to large-scale applications covering areas of several hundred square kilometers or time series covering observation periods of several months. The analysis and interpretation of the accumulating large volume of digital images or videos will continue to challenge the marine science community to keep this process efficient and effective. It is safe to say that any strategy will rely on some software platform supporting manual image and video annotation, either for a direct manual annotation-based analysis or for collecting training data to deploy a machine learning–based approach for (semi-)automatic annotation. This paper describes how computer-assisted manual full-frame image and video annotation is currently performed in marine science and how it can evolve to keep up with the increasing demand for image and video annotation and the growing volume of imaging data. As an example, observations are presented how the image and video annotation tool BIIGLE 2.0 has been used by an international community of more than one thousand users in the last 4 years. In addition, new features and tools are presented to show how BIIGLE 2.0 has evolved over the same time period: video annotation, support for large images in the gigapixel range, machine learning assisted image annotation, improved mobility and affordability, application instance federation and enhanced label tree collaboration. The observations indicate that, despite novel concepts and tools introduced by BIIGLE 2.0, full-frame image and video annotation is still mostly done in the same way as two decades ago, where single users annotated subsets of image collections or single video frames with limited computational support. We encourage researchers to review their protocols for education and annotation, making use of newer technologies and tools to improve the efficiency and effectivity of image and video annotation in marine science.

scholarly journals SPRING POINT DETECTION OF HIGH RESOLUTION IMAGE BASED ON YOLOV3

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 645-650
Author(s):  
J. Wu ◽  
Z. Zhang ◽  
G. Huang ◽  
G. Ma
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Image Annotation ◽  
Remote Sensing Image ◽  
Acquisition Time ◽  
Data Set ◽  
Sensing Technology ◽  
Belt And Road ◽  
Point Detection ◽  
The Belt And Road

Abstract. The Xinjiang region of China is a vast and sparsely populated area with complex topography, surrounded by basins and mountains, and its geomorphological features and water circulation process make the traditional spring water resource acquisition time-consuming, labor-consuming and inaccurate. Remote Sensing Technology has the advantages of large scale, periodicity, timeliness and comprehensiveness in target detection. In order to realize the artificial intelligence detection of springs in Xinjiang, this paper presents a method of detecting springs in remote sensing image based on the YOLOV3 network framework, based on the data set of 512 * 512 by using 0.8m remote sensing image annotation, a model of recognition of spring point based on Yolov3 network is constructed and trained. The results show that the map of spring point is 0.973, which is the basis of monitoring and protecting the natural environment in the Belt and Road Initiatives.

scholarly journals Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3094
Author(s):  
Vitor Fortes Rey ◽  
Kamalveer Kaur Garewal ◽  
Paul Lukowicz
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Activity Recognition ◽  
Language Processing ◽  
Large Scale ◽  
Simulated Data ◽  
Training Data ◽  
Sensor Data ◽  
Activity Data ◽  
Data Set

Human activity recognition (HAR) using wearable sensors has benefited much less from recent advances in Deep Learning than fields such as computer vision and natural language processing. This is, to a large extent, due to the lack of large scale (as compared to computer vision) repositories of labeled training data for sensor-based HAR tasks. Thus, for example, ImageNet has images for around 100,000 categories (based on WordNet) with on average 1000 images per category (therefore up to 100,000,000 samples). The Kinetics-700 video activity data set has 650,000 video clips covering 700 different human activities (in total over 1800 h). By contrast, the total length of all sensor-based HAR data sets in the popular UCI machine learning repository is less than 63 h, with around 38 of those consisting of simple mode of locomotion activities like walking, standing or cycling. In our research we aim to facilitate the use of online videos, which exist in ample quantities for most activities and are much easier to label than sensor data, to simulate labeled wearable motion sensor data. In previous work we already demonstrated some preliminary results in this direction, focusing on very simple, activity specific simulation models and a single sensor modality (acceleration norm). In this paper, we show how we can train a regression model on generic motions for both accelerometer and gyro signals and then apply it to videos of the target activities to generate synthetic Inertial Measurement Units (IMU) data (acceleration and gyro norms) that can be used to train and/or improve HAR models. We demonstrate that systems trained on simulated data generated by our regression model can come to within around 10% of the mean F1 score of a system trained on real sensor data. Furthermore, we show that by either including a small amount of real sensor data for model calibration or simply leveraging the fact that (in general) we can easily generate much more simulated data from video than we can collect its real version, the advantage of the latter can eventually be equalized.

GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins

2020 ◽  
Author(s):  
Wanshan Ning ◽  
Peiran Jiang ◽  
Yaping Guo ◽  
Chenwei Wang ◽  
Xiaodan Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Quality ◽  
Large Scale ◽  
Sequence Similarity ◽  
Academic Research ◽  
Computational Prediction ◽  
Structural Features ◽  
Training Data ◽  
Data Set ◽  
Graphic Presentation

Abstract As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S-palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S-palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S-palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S-palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/.

scholarly journals The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification

2019 ◽  
Vol 11 (19) ◽  
pp. 2249 ◽  
Author(s):  
Patrick Leinenkugel ◽  
Ramona Deck ◽  
Juliane Huth ◽  
Marco Ottinger ◽  
Benjamin Mack
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Natura 2000 ◽  
High Potential ◽  
Training Data ◽  
Data Generation ◽  
Data Set ◽  
Similar Accuracy ◽  
Landsat Satellite

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.

Sign in / Sign up

Export Citation Format

Share Document