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A method of building a geo-tree includes collecting a plurality of data items having Original Assignee: Telefonaktiebolaget LM Ericsson AB; Priority date. People added by hdhdhd: Advanced Search. Name · Institution · Area · Added by · Date · Dallas B. Dunlap (Info), UT Austin, hdhdhd, ‑08‑ Zane R. Jobe. GeoTree: using spatial information for georeferenced video search . Pages, 1- Publication Date, (yyyy-mm-dd). Publisher, Elsevier Science.
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- US20130290332A1 - Method of Building a Geo-Tree - Google Patents
Data items can be tagged with geographic location information or geo tags or geo data and contextual information or context tags or context data. An example of a data item may be a media item in the form of digital images and movie clips. An example of such a sensor is a temperature sensor installed in a building, which temperature sensor may be associated with a data item in the form of a file or record in a sensor database. Alternatively or in addition thereto the geographical location information may comprise a street address, building name, floor level, room name, IP-address etc.
As understood by the person skilled in the art, the examples of contextual information mentioned above for the temperature sensor could be used in an analogous manner for other types of sensors independently of their complexity.
Examples of other sensors may be velocity sensors and accelerometers embedded in vehicles, image sensors such as cameras installed in a vehicle, mobile telephone or in a building, and energy consumption sensors installed in a vehicle, mobile telephone or in a building.
An exemplary data item is illustrated in FIG. Data item may have geo tag and context tag associated with it. A geo tag may include location information e. A context tag may include descriptive terms e. Exemplary data items are illustrated in FIGS. One example of a data item as described above may be a media item. A data item may be associated with geo data and context data If data item A of FIG.
The subject of this image i. Grand Canyon or a bridge can be included in the context tag. The location of the bridge can be represented by the geo tag using latitude, longitude and altitude coordinates. In general, exemplary embodiments may include collecting data, clustering the collected data, classifying the clusters and building a geo tree.
Data may here be collected from an available database of items tagged with geographic and context information. Media items, for example, are available and can be accessed or collected from databases over the internet. Any archive having data tagged with location geo information and context information could be used to collect the data.
The database of items need not be limited to being accessible over the internet—it could also be available offline. This information in the form of a table for example may be stored in a database. Utilizing the data items of FIGS. This process could potentially result in a particular context tag being associated with multiple geo tags i.
For example, a context tag for a city name may be associated with multiple geo tags each representing a different point within the city.
Similarly, a particular geo tag i. For example, a location co-ordinate may be associated with a street, a neighborhood or part of a city, a city, a state, a country, etc. Again, this may depend on the level of granularity used for the location co-ordinates. A cluster analysis on the collected context tags may be performed to determine if the context tags are positioned in clusters. This may take place if multiple geo tags are associated with a context tag.
Each cluster of positions may be used to calculate an approximate area of the place associated with the context tag. The area of a cluster may be computed by connecting all the location co-ordinates associated with a particular context tag for example. In some embodiments, a distance between contextual tags i. Certain contextual tags may not have clusters.
This may occur if only a single location co-ordinate is associated with a context tag for example. This may also occur with generic context tags which may be associated with vastly different geo tags.
As a result, it would be hard to classify this context tag into a cluster. In this case, this context tag may be considered not to be associated with a geo tag. Associating context tags and geo tags may result in the context tags being spread out on a map creating different density groups based on the geographical position geo tag and text description context tag.
A hyperplane may be identified and used to separate the density groups into clusters. Other approaches for clustering data may include K-means clustering, hierarchical clustering, Euclidean clustering, etc.
The process of identifying a hyperplane and the clusters may be an iterative process. Initially, big regions may be separated from each other similar to building from top down.
Initially, the hemispheres southern and northern or eastern and western portions of the world may be separated from each other for example. Similarly, continents or countries may be separated from each other. Subsequently, a more detailed or smaller area hyperplane may be identified for further separating the densities into smaller clusters.
As a result, some clusters may be included within other clusters. For example, the cluster for Sweden may be included within the cluster for Europe.
The country cluster such as Sweden may include clusters for cities Stockholm, Gothenburg or Goteborg, Malmo, etc. If a first cluster overlaps more than a predetermined percentage of a second cluster, then the second cluster may be considered as belonging to the first cluster. This may be true for a neighborhood in a city where the cluster for the neighborhood may be considered to be part or, mostly part of the cluster for the city.
The clusters may then be classified. Each cluster may be classified by the context tag. When the clusters have been classified, the geographical tree with tags may be built or created. The geographic tree may be stored on a computer readable medium such as, but not limited to, a database for example.
The database may be associated with a server.
File:mawatari.info - Wikimedia Commons
Each context tag may have a hierarchical path connecting it to other context tags. Some examples of a hierarchical geo tree structure may include: A method in accordance with exemplary embodiments is illustrated in FIG. Data may be collected at step Clusters for the collected data may be determined at The clustered data may be classified at A geo-tree based on the classified data may be built at The geo-tree may be stored on a computer readable medium at The geo-tree may be used to rate the geographical meaning and importance of text content that has not been geographically tagged.
Once the geo-tree is created, it may be utilized to classify content that does not have geographic tags associated therewith. That is, the geo-tree may be utilized to automatically identify words that have a geographic importance in a text description metadata and then to classify the content to the highest relative geographic position.
USA1 - Method of Building a Geo-Tree - Google Patents
That is, a data item that is only tagged with contextual data and not geographic data may be associated with a node based on context data after the geo-tree has been built and establish a geographic context for the data item.
A geo-tree in accordance with exemplary embodiments is illustrated in FIG. Each cluster may also have a cluster score which may simply be the same as the level in the geo-tree. A user equipment such as a mobile computing device may build a geo-tree in exemplary embodiments; it may also be built in a server at a network node. The data may be available in the computing device or server respectively or it may be obtained from a database.
A user equipment is illustrated in FIG. Such a user equipment may be a mobile telephone or a portable computing device for example. The user equipment may include, inter alia, transceiverprocessor and computer readable medium in the form of a memory. Once the data items are available to the user equipmentthe processor may perform the collection, clustering, classification and the building of the geo-tree in the manner described above according to exemplary embodiments. In one embodiment, in order for the processor to be able to perform the steps illustrated in FIG.
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