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https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15993449#comment-15993449
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ASF GitHub Bot commented on TIKA-2322:
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smadha commented on issue #168: fix for TIKA-2322 contributed by msha...@usc.edu
URL: https://github.com/apache/tika/pull/168#issuecomment-298719232
 
 
   Can we add this inside docker file? Looks like we can add this piece of code 
inside docker file 
-https://github.com/apache/tika/blob/e141640891cd7adcfc1848b351c0db7eab00a2d2/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py#L189-L202
   
   It looks like we need to download [model 
tar](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)  
[synsets.txt](https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_lsvrc_2015_synsets.txt)
 [model 
metadata](https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_metadata.txt)
 in `/tmp/imagenet`
   
   Will this not work? @thammegowda @ThejanW 
 
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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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