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https://issues.apache.org/jira/browse/SPARK-8488?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feynman Liang updated SPARK-8488:
---------------------------------
Description:
Histogram of oriented gradients (HOG) is method utilizing local orientation
(gradients and edges) to transform images into dense image descriptors (Dalal &
Triggs, CVPR 2005,
http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf).
HOG in Spark ML pipelines can be implemented as a
org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the
transformer should output an ArrayArray[[Numeric]] of the HOG features for the
provided image.
HOG and SIFT are similar in that the both represent images using local
orientation histograms. In contrast to SIFT, however, HOG uses overlapping
spatial blocks and is computed densely across all pixels.
was:
Histogram of oriented gradients (HOG) is method utilizing local orientation
(gradients and edges) to transform images into dense image descriptors (Dalal &
Triggs, CVPR 2005,
http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf).
HOG in Spark ML pipelines can be implemented as a
org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the SIFT
transformer should output an ArrayArray[[Numeric]] of the HOG features for the
provided image.
HOG and SIFT are similar in that the both represent images using local
orientation histograms. In contrast to SIFT, however, HOG uses overlapping
spatial blocks and is computed densely across all pixels.
> HOG Feature Transformer
> -----------------------
>
> Key: SPARK-8488
> URL: https://issues.apache.org/jira/browse/SPARK-8488
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Feynman Liang
> Priority: Minor
>
> Histogram of oriented gradients (HOG) is method utilizing local orientation
> (gradients and edges) to transform images into dense image descriptors (Dalal
> & Triggs, CVPR 2005,
> http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf).
> HOG in Spark ML pipelines can be implemented as a
> org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the
> transformer should output an ArrayArray[[Numeric]] of the HOG features for
> the provided image.
> HOG and SIFT are similar in that the both represent images using local
> orientation histograms. In contrast to SIFT, however, HOG uses overlapping
> spatial blocks and is computed densely across all pixels.
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