[jira] [Commented] (SPARK-8486) SIFT Feature Transformer

["K S Sreenivasa Raghavan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14648712#comment-14648712 ] 

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

Hello,

             I am new to contribution, but I have some experience in PySpark as I took some courses in Edx. Can you explain me about this? Please mentor me.

> 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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