Space-Efficient Representations of Raster Time Series

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil contamination. Many different sensors are generating this type of data, which makes such analyses possible, but also challenges the technological capacity to store and retrieve the data. In this work, we propose a space-efficient representation of raster time series that is based on Compact Data Structures (CDS). Our method uses a strategy of snapshots and logs to represent the data, in which both components are represented using CDS. We study two variants of this strategy, one with regular sampling and another one based on a heuristic that determines at which timestamps should the snapshots be created to reduce the space redundancy. We perform a comprehensive experimental evaluation using real datasets. The results show that the proposed strategy is competitive in space with alternatives based on pure data compression, while providing much more efficient query times for different types of queries.The data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Funding: CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). This work was also supported by Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14]; Ministerio de Ciencia, Innovación y Universidades under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7; PID2019- 105221RB-C41/AEI/10.13039/501100011033]; ANID - Millennium Science Initiative Program - Code ICN17_002; Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED) [Grant No. 519RT0579]Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240.2020.1.185Xunta de Galicia; IN852A 2018/14Chile. Agencia Nacional de Investigación y Desarrollo; ICN17_00

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure

3D exemplar-based image inpainting in electron microscopy

In electron microscopy (EM) a common problem is the non-availability of data, which causes artefacts in reconstructions. In this thesis the goal is to generate artificial data where missing in EM by using exemplar-based inpainting (EBI). We implement an accelerated 3D version tailored to applications in EM, which reduces reconstruction times from days to minutes. We develop intelligent sampling strategies to find optimal data as input for reconstruction methods. Further, we investigate approaches to reduce electron dose and acquisition time. Sparse sampling followed by inpainting is the most promising approach. As common evaluation measures may lead to misinterpretation of results in EM and falsify a subsequent analysis, we propose to use application driven metrics and demonstrate this in a segmentation task. A further application of our technique is the artificial generation of projections in tiltbased EM. EBI is used to generate missing projections, such that the full angular range is covered. Subsequent reconstructions are significantly enhanced in terms of resolution, which facilitates further analysis of samples. In conclusion, EBI proves promising when used as an additional data generation step to tackle the non-availability of data in EM, which is evaluated in selected applications. Enhancing adaptive sampling methods and refining EBI, especially considering the mutual influence, promotes higher throughput in EM using less electron dose while not lessening quality.Ein häufig vorkommendes Problem in der Elektronenmikroskopie (EM) ist die Nichtverfügbarkeit von Daten, was zu Artefakten in Rekonstruktionen führt. In dieser Arbeit ist es das Ziel fehlende Daten in der EM künstlich zu erzeugen, was durch Exemplar-basiertes Inpainting (EBI) realisiert wird. Wir implementieren eine auf EM zugeschnittene beschleunigte 3D Version, welche es ermöglicht, Rekonstruktionszeiten von Tagen auf Minuten zu reduzieren. Wir entwickeln intelligente Abtaststrategien, um optimale Datenpunkte für die Rekonstruktion zu erhalten. Ansätze zur Reduzierung von Elektronendosis und Aufnahmezeit werden untersucht. Unterabtastung gefolgt von Inpainting führt zu den besten Resultaten. Evaluationsmaße zur Beurteilung der Rekonstruktionsqualität helfen in der EM oft nicht und können zu falschen Schlüssen führen, weswegen anwendungsbasierte Metriken die bessere Wahl darstellen. Dies demonstrieren wir anhand eines Beispiels. Die künstliche Erzeugung von Projektionen in der neigungsbasierten Elektronentomographie ist eine weitere Anwendung. EBI wird verwendet um fehlende Projektionen zu generieren. Daraus resultierende Rekonstruktionen weisen eine deutlich erhöhte Auflösung auf. EBI ist ein vielversprechender Ansatz, um nicht verfügbare Daten in der EM zu generieren. Dies wird auf Basis verschiedener Anwendungen gezeigt und evaluiert. Adaptive Aufnahmestrategien und EBI können also zu einem höheren Durchsatz in der EM führen, ohne die Bildqualität merklich zu verschlechtern

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Efficient Analysis in Multimedia Databases

The rapid progress of digital technology has led to a situation where computers have become ubiquitous tools. Now we can find them in almost every environment, be it industrial or even private. With ever increasing performance computers assumed more and more vital tasks in engineering, climate and environmental research, medicine and the content industry. Previously, these tasks could only be accomplished by spending enormous amounts of time and money. By using digital sensor devices, like earth observation satellites, genome sequencers or video cameras, the amount and complexity of data with a spatial or temporal relation has gown enormously. This has led to new challenges for the data analysis and requires the use of modern multimedia databases. This thesis aims at developing efficient techniques for the analysis of complex multimedia objects such as CAD data, time series and videos. It is assumed that the data is modeled by commonly used representations. For example CAD data is represented as a set of voxels, audio and video data is represented as multi-represented, multi-dimensional time series. The main part of this thesis focuses on finding efficient methods for collision queries of complex spatial objects. One way to speed up those queries is to employ a cost-based decompositioning, which uses interval groups to approximate a spatial object. For example, this technique can be used for the Digital Mock-Up (DMU) process, which helps engineers to ensure short product cycles. This thesis defines and discusses a new similarity measure for time series called threshold-similarity. Two time series are considered similar if they expose a similar behavior regarding the transgression of a given threshold value. Another part of the thesis is concerned with the efficient calculation of reverse k-nearest neighbor (RkNN) queries in general metric spaces using conservative and progressive approximations. The aim of such RkNN queries is to determine the impact of single objects on the whole database. At the end, the thesis deals with video retrieval and hierarchical genre classification of music using multiple representations. The practical relevance of the discussed genre classification approach is highlighted with a prototype tool that helps the user to organize large music collections. Both the efficiency and the effectiveness of the presented techniques are thoroughly analyzed. The benefits over traditional approaches are shown by evaluating the new methods on real-world test datasets

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

Efficient processing of raster and vector data

[Abstract] In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.Ministerio de Ciencia, Innovación y Universidades; TIN2016-78011-C4-1-RMinisterio de Ciencia, Innovación y Universidades; TIN2016-77158 C4-3-RMinisterio de Ciencia, Innovación y Universidades; RTC-2017-5908-7Xunta de Galicia; ED431C 2017/58Xunta de Galicia; ED431G/01Xunta de Galicia; IN852A 2018/14University of Bío-Bío; 192119 2/RUniversity of Bío-Bío; 195119 GI/V