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Posted to issues@spark.apache.org by "Feynman Liang (JIRA)" <ji...@apache.org> on 2015/07/13 05:34:04 UTC
[jira] [Updated] (SPARK-8488) HOG Feature Transformer
[ https://issues.apache.org/jira/browse/SPARK-8488?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
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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