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From "Hudson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2322) Video labeling using existing ObjectRecognition
Date Sun, 30 Apr 2017 23:00:08 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15990493#comment-15990493
] 

Hudson commented on TIKA-2322:
------------------------------

SUCCESS: Integrated in Jenkins build Tika-trunk #1250 (See [https://builds.apache.org/job/Tika-trunk/1250/])
fix for TIKA-2322 contributed by msharan@usc.edu (mattmann: [https://github.com/apache/tika/commit/6c903f25aa6508d06fc6f0044ac996ff9e9d9bf4])
* (edit) tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
* (add) tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
* (edit) tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTRecogniser.java
* (add) tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTVideoRecogniser.java
* (add) tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
* (add) tika-parsers/src/test/resources/test-documents/testVideoMp4.mp4
* (add) tika-parsers/src/test/java/org/apache/tika/parser/recognition/tf/TensorflowVideoRecParserTest.java
Record change for TIKA-2322. (mattmann: [https://github.com/apache/tika/commit/b19b9c36986707db8755a62ef77923af01549411])
* (edit) CHANGES.txt


> 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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