369 research outputs found
SVS-JOIN : efficient spatial visual similarity join for geo-multimedia
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN B by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN G is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN Q is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently
Multi-scale data storage schemes for spatial information systems
This thesis documents a research project that has led to the design and prototype
implementation of several data storage schemes suited to the efficient multi-scale
representation of integrated spatial data. Spatial information systems will benefit from
having data models which allow for data to be viewed and analysed at various levels
of detail, while the integration of data from different sources will lead to a more
accurate representation of reality.
The work has addressed two specific problems. The first concerns the design of an
integrated multi-scale data model suited for use within Geographical Information
Systems. This has led to the development of two data models, each of which allow for
the integration of terrain data and topographic data at multiple levels of detail. The
models are based on a combination of adapted versions of three previous data
structures, namely, the constrained Delaunay pyramid, the line generalisation tree and
the fixed grid.
The second specific problem addressed in this thesis has been the development of an
integrated multi-scale 3-D geological data model, for use within a Geoscientific
Information System. This has resulted in a data storage scheme which enables the
integration of terrain data, geological outcrop data and borehole data at various levels
of detail.
The thesis also presents details of prototype database implementations of each of the
new data storage schemes. These implementations have served to demonstrate the
feasibility and benefits of an integrated multi-scale approach.
The research has also brought to light some areas that will need further research before
fully functional systems are produced. The final chapter contains, in addition to
conclusions made as a result of the research to date, a summary of some of these areas
that require future work
Advance of the Access Methods
The goal of this paper is to outline the advance of the access methods in the last ten years as well as
to make review of all available in the accessible bibliography methods
Feature Extraction Using Fractal Codes
Fast and successful searching for an object in a multimedia database is a highly desirable functionality. Several approaches to content based retrieval for multimedia databases can be found in the literature [9,10,12,14,17]. The approach we consider is feature extraction. A feature can be seen as a way to present simple information like the texture, color and spatial information of an image, or the pitch, frequency of a sound etc.
In this paper we present a method for feature extraction on texture and spatial similarity, using fractal coding techniques. Our method is based upon the observation that the coefficients describing the fractal code of an image, contain very useful information about the structural content of the image. We apply simple statistics on information produced by fractal image coding. The statistics reveal features and require a small amount of storage. Several invariances are a consequence of the used methods: size, global contrast, orientation
Monitoring and Detection of Hotspots using Satellite Images
Nowadays, the usage of optical remote sensing NOAA-AVHRR satellite data
is getting familiar as it is known can save cost in order to capture a wide coverage of
ground image. The captured images are meaningful after several processes done
over it to produce hotspot detection. Developing a specific database to store
information of Hotspots (LAC images) would make datamanagement and archiving
purpose in more efficient and systematic way. Real-time data gathered are monitored
countries such as Malaysia, Thailand, Singapore, Indonesia and Brunei within the
region of NOAA Satellite coverage area. PostGIS, PostgreSQL, Mapserver and
Autodesk MapGuide Studio software are to be studied as a guide to develop a
system with simple database using object-relational database management system to
store raster and vector images. This paper describes a solution for efficient handling
of large raster image data sets in a standard object-relational database management
system. By means of adequate indexing, retrieval techniques and multi resolution
cell indexing (Quad-Tree) can be achieved using a standard DBMS, even for very
large satellite images. Single image will be divided equally into 64 small squares (3
levels of image hierarchy - each level has 4 sub images of the higher image). Partial
information of Daily Haze report (processed Hotspot on image map) produces by
NREB can be viewed using web-based application. The final product of this project
is a web-based application for displaying Hotspots on maps (combination of raster
and vector images) with the ability to search record from database and functions to
zoom in or zoom out the map. The objective of this paper is also to show the way
satellite images and descriptive information are combined and amalgamated to form
an Internet or Intranet application
Geographic Information Systems: The Developer\u27s Perspective
Geographic information systems, which manage data describing the surface of the earth, are becoming increasingly popular. This research details the current state of the art of geographic data processing in terms of the needs of the geographic information system developer. The research focuses chiefly on the geographic data model--the basic building block of the geographic information system. The two most popular models, tessellation and vector, are studied in detail, as well as a number of hybrid data models.
In addition, geographic database management is discussed in terms of geographic data access and query processing. Finally, a pragmatic discussion of geographic information system design is presented covering such topics as distributed database considerations and artificial intelligence considerations
Extending General Compact Querieable Representations to GIS Applications
The raster model is commonly used for the representation of images in many
domains, and is especially useful in Geographic Information Systems (GIS) to
store information about continuous variables of the space (elevation,
temperature, etc.). Current representations of raster data are usually designed
for external memory or, when stored in main memory, lack efficient query
capabilities. In this paper we propose compact representations to efficiently
store and query raster datasets in main memory. We present different
representations for binary raster data, general raster data and time-evolving
raster data. We experimentally compare our proposals with traditional storage
mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show
that our structures are up to 10 times smaller than classical linear quadtrees,
and even comparable in space to non-querieable representations of raster data,
while efficiently answering a number of typical queries.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sklodowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941
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