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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2322) Video labeling using existing ObjectRecognition
Date Thu, 27 Apr 2017 04:51:04 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15985998#comment-15985998
] 

ASF GitHub Bot commented on TIKA-2322:
--------------------------------------

chrismattmann commented on issue #168: fix for TIKA-2322 contributed by msharan@usc.edu
URL: https://github.com/apache/tika/pull/168#issuecomment-297611010
 
 
   OK, here are two things we need to put in the wiki guide for this (and also we need to
update the TikaAndVision page with this):
   
   * On Mac, [Docker Containers Don't Run on the Host Machine itself](http://stackoverflow.com/questions/35878297/cant-connect-to-docker-containers-on-osx).
This means that in order to use `localhost` like we specify in our instructions, you have
to fiddle with Virtual Box (which is the VM server that Docker, and Docker-Machine work with).
   * To fiddle with Virtual Box, and get it to expose our port, 8764, you need to [enable
and add the specified port forwarding rules to your Virtual Box default machine](https://jhipster.github.io/tips/020_tip_using_docker_containers_as_localhost_on_mac_and_windows.html).
You can find instructions on how to do that in the previous link, and you should add a rule
for 8764 and 8764, and map it to 127.0.0.1. After that you're set!
   
 
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> Video labeling using existing ObjectRecognition
> -----------------------------------------------
>
>                 Key: TIKA-2322
>                 URL: https://issues.apache.org/jira/browse/TIKA-2322
>             Project: Tika
>          Issue Type: Improvement
>          Components: parser
>            Reporter: Madhav Sharan
>            Assignee: Chris A. Mattmann
>              Labels: memex
>             Fix For: 1.15
>
>
> Currently TIKA supports ObjectRecognition in Images. I am proposing to extend this to
support videos. 
> Idea is -
> 1. Extract frames from video and run IncV3 to get labels for these frames. 
> 2. We average confidence scores of same labels for each frame. 
> 3. Return results in sorted order of confidence score. 
> I am writing code for different modes of frame extractions -
> 1. Extract center image.
> 2. Extract frames after every fixed interval.
> 3. Extract N frames equally divided across video.
> We used this approach in [0]. Code in [1]
> [0] https://github.com/USCDataScience/hadoop-pot
> [1] https://github.com/USCDataScience/video-recognition



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