Video search

A video search engine is a web-based search engine
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which crawls
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the web for video
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content. Some video fish engines parse externally hosted limit while others allowance limit to be exchange and hosted on their own servers. Some engines also allowance users to fish by video format type and by length of the clip. Search results are usually attach to by a thumbnail view of the video.
restrict 1 Utility
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2 fish criterion
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2.1 Metadata
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2.1.1 inner metadata
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2.1.2 outer metadata
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2.1.2.1 label and Description
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2.1.2.2 Filename
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2.1.2.3 Tags
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2.1.2.4 Transcription and subtitles
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2.2 Speech Recognition
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2.3 Text Recognition
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2.4 enclose Analysis
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3 be criterion
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3.1 ordering by relevance
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3.2 ordering by go out of upload
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3.3 ordering by be of views
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3.4 ordering by user rating
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4 Interfaces
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4.1 Internet
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4.1.1 Repositories with video searcher
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4.1.2 video searchers repositories
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4.2 clannish Network
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5 engineered and algorithms
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6 favorite video fish engines
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6.1 Agnostic search
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6.2 Non-agnostic search
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7 See also
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8 References
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9 outer links
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The important use of these search engines is the increasing creation of audiovisual content and the need to manage it properly. The digitization of audiovisual collect and the establishment of the Internet, has led to large quantities of video files have in big databases, whose recovery can be very difficult because of the ample volumes of data and the existence of a semantic gap.
fish criterion
Metadata is information about facts. It could be information about who is the author of the video, creation date, duration, and all the information you would like to extract and include in the same files. Internet is frequently used in a language called XML to encrypted metadata, which works very well through the web and is legible by people. Thus, through this information contained in these files is the easiest way to find data of interesting to us.
All video change incorporate their own metadata. The title, description, coding quality or transcription of the content are possible. To analyze these data exist programs like FLV MetaData Injector, Sorenson Squeeze or Castfire. all one has some utilities and special specifications.
In most casing you essential use the aforesaid mechanisms as in the put of an visualize or text content.
label and Description
It should be descriptive, include keywords that outlining the video with no requires to see their label or description. Ideally, separate the words by rush "-".
stick
Although not totally standard, at that place are two formats that have information in a interim component that is specified, one for subtitles and different for transcripts, which can besides be employed for subtitles.
video search
video search
smoke fish for videos by investigate express recognition
Speech recognition consists of a transcript of the speech of the audio track of the videos, creating a text file. In this way and with the encouraging of a word extractor can elementary fish if the video limit is of our interest.
video search
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smoke fish for videos with text recognition "TalkMiner"
The text recognition can be very multipurpose to recognize characters in the videos through "chyrons". As with speech recognizers, at that place are fish engines that allow, through character recognition, to vie a video from a specific point where you see the vent you want.
doner the visual descriptors
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we can analyze the enclose of a video and digging information that can be get as metadata. Descriptions are generated mechanically and can outlining other aspects of the frames, much as color, texture, shape, motion, and the situation.
be criterion
Most fish engines use other methods to group the ensue and provide the best video in the archetypal results. However, most slot allowance you to decide the ensue by several criteria.
ordering by relevance
This is a criterion based completely on the timeline where you can decide the ensue match to their seniority in the repository.
ordering by be of reasons
It is common practice in repositories let the users sequenced the videos, so that a content of banner and relevance will have a advanced been on the inventory of ensue gaining visibility. This practice is intimately think to virtual communities.
Interfaces
indoors Internet interfaces we can find repositories that host video files which integrated a fish engine that searches single their own databases, and video searchers without repository that fish in sources of external software.
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Repository with video searcher "Dailymotion" Repositories with video searcher
Their searches are often based on reading the metadata tags, titles and descriptions that users assign to their videos. The disposal and order criterion of the results of these searches are usually selectable betwixt the file delegate date, the number of viewings or what they call the relevance. Still, sorting criterion are nowadays the main weapon of these websites, because in terms of promotion is dead important the positioning that they can give to your video.
video searchers repositories
You can also determine the case where a search engine only searches in audiovisual show have indoors a computer or, as it happens in televisions, on a private server where users access through a local area network. These searchers are usually software or rich Internet applications with a very specific search options for maximum speed and efficiency when display the results. They are typically used for large databases and are therefore highly focused to satisfy the needs of television companies. An example of this type of software would be the Digition Suite, which apart from being a benchmark in this benignant of interfaces is very close to us as for the storage and retrieval show system from the CorporaciĆ³ Catalana de Mitjans Audiovisuals
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.
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Video fish has create by mental act decrease through any basal fish formats which existing nowadays and all use keywords
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. The keywords for each search can be found in the label of the media, any text attached to the media and content think web pages, besides be by write and users of video hosts resources.
It is by and large suggested that speech to text is possible, though recently Thomas Wilde, the new CEO of Everyzing, suggested that Everyzing works 70% of the quantify when there is music, ambient noise or more than one person speaking. If newscast style network is available, that can tower to 93%. .
It is generally suggested that optic fish into video does not work well and that no company is using it publicly. Researchers at UC San Diego and Carnegie Mellon University have been work on the optic fish problem for more than than 15 years, and admitted at a "Future of Search" conference at UC Berkeley in the Spring of 2007 that it was years away from be viable flat in simple search.
favorite video fish engines Agnostic fish
fish ensue are modified, or suspect, due to the ample hosts video be computerized beneficial treatment in fish results:
AOL
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Video provide a leading video search engine that can be employed to determine video determine on favorite video destinations across the web. In December 2005, AOL acquired Truveo
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video search. Google Videos
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is a favorite video fish engine which employed to recognizes its visitors to upload videos. It desire YouTube and galore variant video hosts sites. Yahoo!
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Video fish Yahoo!'s fish engine investigates video show on the internet use its Media RSS
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standard. Is open on a operating reasons label "Video" off the important summon preparatory the text block. See besides Content-based visualize retrieval
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Metadata
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Optical scratch recognition
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Search engine
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Search engine optimization
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Speech recognition
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write ^
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SEO by investigate principal webmaster
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^
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Digitalize or die
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^
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Digition Suite from Activa Multimedia
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outer think Process of fish engines How fill Works
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Text consult in CorporaciĆ³ Catalana de Mitjans Audiovisuals
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German Video fish engine youtube videos
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