scholarly journals Concept Mask: Large-Scale Segmentation from Semantic Concepts

2018 ◽  
pp. 542-557
Author(s):  
Yufei Wang ◽  
Zhe Lin ◽  
Xiaohui Shen ◽  
Jianming Zhang ◽  
Scott Cohen
Keyword(s):  
Large Scale ◽  
Semantic Concepts

scholarly journals 2166

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12
Author(s):  
Jianyin Shao ◽  
Ram Gouripeddi ◽  
Julio C. Facelli
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Power Distribution ◽  
Large Scale ◽  
Search Space ◽  
Free Text ◽  
Eligibility Criteria ◽  
Clinical Notes ◽  
Semantic Concepts ◽  
Mimic Iii

OBJECTIVES/SPECIFIC AIMS: This poster presents a detailed characterization of the distribution of semantic concepts used in the text describing eligibility criteria of clinical trials reported to ClincalTrials.gov and patient notes from MIMIC-III. The final goal of this study is to find a minimal set of semantic concepts that can describe clinical trials and patients for efficient computational matching of clinical trial descriptions to potential participants at large scale. METHODS/STUDY POPULATION: We downloaded the free text describing the eligibility criteria of all clinical trials reported to ClinicalTrials.gov as of July 28, 2015, ~195,000 trials and ~2,000,000 clinical notes from MIMIC-III. Using MetaMap 2014 we extracted UMLS concepts (CUIs) from the collected text. We calculated the frequency of presence of the semantic concepts in the texts describing the clinical trials eligibility criteria and patient notes. RESULTS/ANTICIPATED RESULTS: The results show a classical power distribution, Y=210X(−2.043), R2=0.9599, for clinical trial eligibility criteria and Y=513X(−2.684), R2=0.9477 for MIMIC patient notes, where Y represents the number of documents in which a concept appears and X is the cardinal order the concept ordered from more to less frequent. From this distribution, it is possible to realize that from the over, 100,000 concepts in UMLS, there are only ~60,000 and 50,000 concepts that appear in less than 10 clinical trial eligibility descriptions and MIMIC-III patient clinical notes, respectively. This indicates that it would be possible to describe clinical trials and patient notes with a relatively small number of concepts, making the search space for matching patients to clinical trials a relatively small sub-space of the overall UMLS search space. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results showing that the concepts used to describe clinical trial eligibility criteria and patient clinical notes follow a power distribution can lead to tractable computational approaches to automatically match patients to clinical trials at large scale by considerably reducing the search space. While automatic patient matching is not the panacea for improving clinical trial recruitment, better low cost computational preselection processes can allow the limited human resources assigned to patient recruitment to be redirected to the most promising targets for recruitment.

scholarly journals Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images

2019 ◽  
Vol 11 (8) ◽  
pp. 922 ◽  
Author(s):  
Juli Zhang ◽  
Junyi Zhang ◽  
Tao Dai ◽  
Zhanzhuang He
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Dual Graph ◽  
Semantic Features ◽  
Label Graph ◽  
Remote Sensing Images ◽  
Graph Regularization ◽  
Semantic Concepts ◽  
High Level ◽  
Laborious Work

Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-label annotation task as a recommended problem, based on non-negative matrix tri-factorization (NMTF). The labels of remote sensing images can be recommended directly by recovering the image–label matrix. To learn more efficient latent feature matrices, two graph regularization terms are added to NMTF that explore the affiliated relationships on the image graph and label graph simultaneously. In order to reduce the gap between semantic concepts and visual content, both low-level visual features and high-level semantic features are exploited to construct the image graph. Meanwhile, label co-occurrence information is used to build the label graph, which discovers the semantic meaning to enhance the label prediction for unlabeled images. By employing the information from images and labels, the proposed method can efficiently deal with the sparsity and cold-start problem brought by limited image–label pairs. Experimental results on the UCMerced and Corel5k datasets show that our model outperforms most baseline algorithms for multi-label annotation of remote sensing images and performs efficiently on large-scale unlabeled datasets.

Understanding the cultural concerns of libraries based on automatic image analysis

2019 ◽  
Vol 37 (3) ◽  
pp. 419-434
Author(s):  
Heng Ding ◽  
Wei Lu ◽  
Tingting Jiang
Keyword(s):  
Exploratory Study ◽  
Design Methodology ◽  
Large Scale ◽  
State Of The Art ◽  
Automatic Analysis ◽  
Automatic Image Analysis ◽  
Content Type ◽  
Image Dataset ◽  
Learning Technique ◽  
Semantic Concepts

Purpose Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections. Design/methodology/approach In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns. Findings The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives. Originality/value To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.

Correlation-Based Ranking for Large-Scale Video Concept Retrieval

2010 ◽  
Vol 1 (4) ◽  
pp. 60-74 ◽  
Author(s):  
Lin Lin ◽  
Mei-Ling Shyu
Keyword(s):  
Large Scale ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multiple Correspondence Analysis ◽  
Multimedia Data ◽  
Ranking Algorithm ◽  
Video Content ◽  
Semantic Concepts ◽  
Efficient Retrieval ◽  
High Level

Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.

Correlation-Based Ranking for Large-Scale Video Concept Retrieval

2012 ◽  
pp. 28-42
Author(s):  
Lin Lin ◽  
Mei-Ling Shyu
Keyword(s):  
Large Scale ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multiple Correspondence Analysis ◽  
Multimedia Data ◽  
Ranking Algorithm ◽  
Video Content ◽  
Semantic Concepts ◽  
Efficient Retrieval ◽  
High Level

Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.

Probability Association Approach in Automatic Image Annotation

2008 ◽  
pp. 615-626
Author(s):  
Feng Xu ◽  
Yu-Jin Zhang
Keyword(s):  
Large Scale ◽  
Image Annotation ◽  
Public Life ◽  
Image Database ◽  
Web Pages ◽  
Challenging Problem ◽  
Semantic Concepts ◽  
Automated Image Annotation ◽  
High Level ◽  
The Web

Content-based image retrieval (CBIR) has wide applications in public life. Either from a static image database or from the Web, one can search for a specific image, generally browse to make an interactive choice, and search for a picture to go with a broad story or to illustrate a document. Although CBIR has been well studied, it is still a challenging problem to search for images from a large image database because of the well-acknowledged semantic gap between low-level features and high-level semantic concepts. An alternative solution is to use keyword-based approaches, which usually associate images with keywords by either manually labeling or automatically extracting surrounding text from Web pages. Although such a solution is widely adopted by most existing commercial image search engines, it is not perfect. First, manual annotation, though precise, is expensive and difficult to extend to large-scale databases. Second, automatically extracted surrounding text might by incomplete and ambiguous in describing images, and even more, surrounding text may not be available in some applications. To overcome these problems, automated image annotation is considered as a promising approach in understanding and describing the content of images.

A Novel Plausible Model for Visual Perception

2011 ◽  
pp. 428-444
Author(s):  
Zhiwei Shi ◽  
Zhongzhi Shi ◽  
Hong Hu
Keyword(s):  
Visual Perception ◽  
Large Scale ◽  
Experimental Simulation ◽  
Visual Features ◽  
Practical Applications ◽  
Low Level ◽  
Cognitive Problem ◽  
Plausible Model ◽  
Semantic Concepts ◽  
High Level

Traditionally, how to bridge the gap between low-level visual features and high-level semantic concepts has been a tough task for researchers. In this article, we propose a novel plausible model, namely cellular Bayesian networks (CBNs), to model the process of visual perception. The new model takes advantage of both the low-level visual features, such as colors, textures, and shapes, of target objects and the interrelationship between the known objects, and integrates them into a Bayesian framework, which possesses both firm theoretical foundation and wide practical applications. The novel model successfully overcomes some weakness of traditional Bayesian Network (BN), which prohibits BN being applied to large-scale cognitive problem. The experimental simulation also demonstrates that the CBNs model outperforms purely Bottom-up strategy 6% or more in the task of shape recognition. Finally, although the CBNs model is designed for visual perception, it has great potential to be applied to other areas as well.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

Sign in / Sign up

Export Citation Format

Share Document