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From "K S Sreenivasa Raghavan (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-8486) SIFT Feature Transformer
Date Wed, 05 Aug 2015 15:33:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14658377#comment-14658377
] 

K S Sreenivasa Raghavan commented on SPARK-8486:
------------------------------------------------

How to accept this issue?
Should I use scala or python?

> SIFT Feature Transformer
> ------------------------
>
>                 Key: SPARK-8486
>                 URL: https://issues.apache.org/jira/browse/SPARK-8486
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Feynman Liang
>            Priority: Minor
>
> Scale invariant feature transform (SIFT) is a scale and rotation invariant method to
transform images into matrices describing local features. (Lowe, IJCV 2004, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
> We can implement SIFT in Spark ML pipelines as a org.apache.spark.ml.Transformer. Given
an image Array[Array[Numeric]], the SIFT transformer should output an ArrayArray[[Numeric]]
of the SIFT features for the provided image.
> The implementation should support computation of SIFT at predefined interest points,
every kth pixel, and densely (over all pixels). Furthermore, the implementation should support
various approximations for approximating the Laplacian of Gaussian using Difference of Gaussian
(as described by Lowe).



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