scholarly journals FeatSet: A Compilation of Visual Features Extracted from Public Image Datasets

2021 ◽  
Author(s):  
Mirela T. Cazzolato ◽  
Lucas C. Scabora ◽  
Guilherme F. Zabot ◽  
Marco A. Gutierrez ◽  
Caetano Traina Jr. ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Public Image ◽  
Visual Features ◽  
Complex Data ◽  
Shape Representations ◽  
Access Methods ◽  
Data Indexing ◽  
Standard Collection ◽  
Components Analysis ◽  
Image Datasets

In this paper, we present FeatSet, a compilation of visual features extracted from open image datasets reported in the literature. FeatSet has a collection of 11 visual features, consisting of color, texture, and shape representations of the images acquired from 13 datasets. We organized the available features in a standard collection, including the available metadata and labels, when available. We also provide a description of the domain of each dataset included in our collection, with visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA) methods. FeatSet is recommended for supervised and non-supervised learning, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

scholarly journals A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing

Data ◽  
2021 ◽  
Vol 6 (10) ◽  
pp. 108
Author(s):  
Carmine Gambardella ◽  
Rosaria Parente ◽  
Alessandro Ciambrone ◽  
Marialaura Casbarra
Keyword(s):  
Remote Sensing ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Spectral Characteristics ◽  
Spectral Signature ◽  
Complex Data ◽  
Ancillary Data ◽  
Spectral Angle Mapper ◽  
Visible Images ◽  
Components Analysis

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza’s aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations.

EPOS-Norway Portal

2021 ◽  
Author(s):  
Jan Michalek ◽  
Kuvvet Atakan ◽  
Christian Rønnevik ◽  
Helga Indrøy ◽  
Lars Ottemøller ◽  
...  
Keyword(s):  
Web Services ◽  
Visual Analysis ◽  
Earth Science ◽  
National Research Council ◽  
Geophysical Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Complex Data ◽  
Submarine Landslides ◽  
Data Repositories

<p>The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e‑infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data – University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.</p><p>In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes.  </p><p>The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.</p>

scholarly journals Generative Warfare Nets: Ensemble via Adversaries and Collaborators

2018 ◽  
Author(s):  
Honglun Zhang ◽  
Liqiang Xiao ◽  
Wenqing Chen ◽  
Yongkun Wang ◽  
Yaohui Jin
Keyword(s):  
Ensemble Learning ◽  
State Of The Art ◽  
Generative Models ◽  
Natural Image ◽  
Complex Data ◽  
Powerful Method ◽  
High Quality ◽  
Two Sides ◽  
Image Datasets

Generative Adversarial Nets are a powerful method for training generative models of complex data, where a Generator and a Discriminator confront with each other and get optimized in a two-player minmax manner. In this paper, we propose the Generative Warfare Nets (GWN) that involve multiple generators and multiple discriminators from two sides to exploit the advantages of Ensemble Learning. We maintain the authorities for the generators and the discriminators to enhance inter-side interactions, and utilize the mechanisms of imitation and innovation to model intra-side interactions among the generators, where they can not only learn from but also compete with each other. Extensive experiments on three natural image datasets show that GWN can achieve state-of-the-art Inception scores and produce diverse high-quality synthetic results.

scholarly journals Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field

2020 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Ke Ren ◽  
Dezhan Qu ◽  
Shaobin Xu ◽  
Xufeng Jiao ◽  
Liang Tai ◽  
...  
Keyword(s):  
Vector Field ◽  
Uncertainty Analysis ◽  
Visual Analysis ◽  
Synthetic Data ◽  
Real Sequence ◽  
Complex Data ◽  
Time Varying ◽  
Data Set ◽  
Spatial Continuity ◽  
Transport Patterns

Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way to compute the uncertainty. However, existing metrics are not accurate enough or are sensitive to outliers; thus, a comprehensive tool for the further analysis of the uncertainty of transport patterns is required. In this paper, we propose a novel framework for quantifying and analyzing the uncertainty of an ensemble vector field. Based on the classical edit distance on real sequence (EDR) method, a robust and accurate metric was proposed to measure the pathline uncertainty. Considering the spatial continuity, we computed the transport variance of the neighborhood of a location, and evaluated the uncertainty correlation between each location and its neighborhood by using the local Moran’s I. Based on the proposed uncertainty measurements, a visual analysis system called UP-Vis (uncertainty pathline visualization) was developed to interactively explore the uncertainty. It provides an overview of the uncertainty and supports detailed exploration of transport patterns at a selected location, and allows for the comparison of transport patterns between a location and its neighborhood. Through pathline clustering, the major trends of the ensemble pathline at a location were extracted. Moreover, a glyph was designed to intuitively display the transport direction and diverging degree of each cluster. For the uncertainty analysis of the neighborhood, a comparison view was designed to compare the transport patterns between a location and its neighborhood in detail. A synthetic data set and weather simulation data set were used in our experiments. The evaluation and case studies demonstrated that the proposed framework can measure the uncertainty effectively and help users to comprehensively explore uncertainty transport patterns.

Statistical Modeling for Ceramic Analysis

2016 ◽  
pp. 57-72
Author(s):  
Gulsebnem Bishop
Keyword(s):  
Complex Data ◽  
Sampling Strategies ◽  
Data Sampling ◽  
Ceramic Analysis ◽  
Chi Square ◽  
Statistical Population ◽  
Archaeological Data ◽  
Principle Components Analysis ◽  
Chi Square Test ◽  
Components Analysis

Statistics can be used to describe, model, and predict archaeological data, provided that the analyst has an understanding of the strengths and limitations of their data type and has a well-defined statistical population. This chapter discusses the major types of archaeological data, sampling strategies, and statistics appropriate for both describing and predicting outcomes for simple and complex ceramic datasets. Description and modeling of complex data can be done with many tools ranging from simple charts and histograms to more complicated methods, such as T-Test, Chi-Square Test, Multi-Response Permutation Procedure (MRPP), and Kernel Density Estimation (KDE), as well as Principle Components Analysis (PCA).

scholarly journals Principal Components Analysis as enhancement Operator and Compression factor

2019 ◽  
Vol 17 (72) ◽  
pp. 25-33
Author(s):  
KhaIiI Ibrahim Kadhim
Keyword(s):  
Principal Components Analysis ◽  
Gaussian Distribution ◽  
Principal Components ◽  
Visual Analysis ◽  
Projection Pursuit ◽  
Orthogonal Projections ◽  
Simplified Approach ◽  
Projection Index ◽  
Hyper Spectral ◽  
Components Analysis

Principal components analysis (PCA) is effective at  compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. Unfortunately, information content in hyper spectral images does not always coincide with such projections. We propose an application of projection pursuit (pp), which seeks to find a set of projections that are "interesting" in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections, a two –step iterative process is followed where we first search for a projection that maximizes a projection index based on the information divergence of the projections estimated probability distribution from the Gaussian distribution and then reduce the rank by projections the data on to the subspace orthogonal to the previous projection . To calculate each projections, we use a simplified approach to maximizing the projection index, which does not require optimization algorithm. It searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. The effectiveness of the  method is demonstrated through simulated examples as well as data from the hyper spectral digital imagery collection experiment and the spatially enhanced broadband and array spectrograph system.

scholarly journals Innovative Approaches for Efficiently Warehousing Complex Data from the Web

Data Mining ◽  
2013 ◽  
pp. 1422-1448
Author(s):  
Fadila Bentayeb ◽  
Nora Maïz ◽  
Hadj Mahboubi ◽  
Cécile Favre ◽  
Sabine Loudcher ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Warehouse ◽  
Design Management ◽  
Complex Data ◽  
Data Warehouses ◽  
Process Data ◽  
Access Methods ◽  
Olap Analysis ◽  
The Web

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.

Using Visual Analysis to Weight Multiple Signatures to Discriminate Complex Data

2011 ◽  
Author(s):  
Renato Bueno ◽  
Daniel S. Kaster ◽  
Humberto L. Razente ◽  
Maria Camila N. Barioni ◽  
Agma J.M. Traina ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Complex Data
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