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From "Feynman Liang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-8486) SIFT/SURF Feature Extractor
Date Fri, 19 Jun 2015 20:21:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Feynman Liang updated SPARK-8486:
---------------------------------
    Description: 
Scale invariant feature transform (SIFT) is a method to transform images into dense vectors
describing local features which are invariant to scale and rotation. (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 Array[Numeric] of the SIFT
features present in the image.

Depending on performance, approximating Laplacian of Gaussian by Difference of Gaussian (traditional
SIFT) as described by Lowe can be even further improved using box filters (aka SURF, see Bay,
ECCV 2006,  http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).

  was:
Scale invariant feature transform (SIFT) is a method to transform images into dense vectors
describing local features which are invariant to scale and rotation. (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 Array[Numeric] of the
SIFT features present in the image.

Depending on performance, approximating Laplacian of Gaussian by Difference of Gaussian (traditional
SIFT) as described by Lowe can be even further improved using box filters (aka SURF, see Bay,
ECCV 2006,  http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).


> SIFT/SURF Feature Extractor
> ---------------------------
>
>                 Key: SPARK-8486
>                 URL: https://issues.apache.org/jira/browse/SPARK-8486
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Feynman Liang
>
> Scale invariant feature transform (SIFT) is a method to transform images into dense vectors
describing local features which are invariant to scale and rotation. (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 Array[Numeric] of the
SIFT features present in the image.
> Depending on performance, approximating Laplacian of Gaussian by Difference of Gaussian
(traditional SIFT) as described by Lowe can be even further improved using box filters (aka
SURF, see Bay, ECCV 2006,  http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).



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