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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2322) Video labeling using existing ObjectRecognition
Date Tue, 02 May 2017 06:52:04 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15992430#comment-15992430
] 

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

ThejanW commented on issue #168: fix for TIKA-2322 contributed by msharan@usc.edu
URL: https://github.com/apache/tika/pull/168#issuecomment-298511180
 
 
   @smadha Could you also add this fact to wiki, when running the container, it always downloads
model files. If a user needs to keep those models, without downloading them again and again,
the could commit those changes to the container. Once committed those changes, running the
container again won't download those models. This is how to do it, 
   
   1. First run the container by this command,
   **docker run -p 8764:8764 -it inception-video-rest-tika**
   2. Open another terminal, without closing the terminal which is server is running, then
type this command,
   **docker ps -l**
   3. User will see the immediate container of inception-video-rest-tika and it's container
id, make note of the container id.
   4. Then by this command, changes will be committed to inception-video-rest-tika container,
   **docker commit inception-video-rest-tika container_id**
   5. To check if it's worked, stop and start the inception-video-rest-tika container again,
it won't download models.
 
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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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