Segment Feature Co-ocurrence Based Texture Detection (bibtex)
by Lech Szumilas, Allan Hanbury
Abstract:
This work on texture detection was inspired by the general problem of object recognition in two dimensional still images. One of the crucial challenges associated with the object recognition is selecting and obtaining discriminative features. In analysis of real scenes, like nature or urban places, many objects contain textures, which can be considered as one of the object features. Texture detection may also significantly improve image segmentation, which is one of the tools used for object recognition. This report presents a novel method named Feature Co-ocurence Texture Detector (FCTD) which allows for fully automatic detection of textures common in real scenes, like water in lakes, tiger skin, fields of flowers or tree crowns. The method searches for an alternating color pattern, like for example black and orange stripes covering tiger skin, which is very often present in those textures. The final result is a hierarchy of textures (described by their boundaries and a set of features) detected at multiple precision levels, which can be used for further analysis or texture classification. It is achieved through hierarchical clustering of color pairs related to adjacent image segments, where each segment represents a low color gradient, simple shaped patch of pixels in the image. The results are presented on some images from the Berkeley database.
Reference:
Segment Feature Co-ocurrence Based Texture Detection (Lech Szumilas, Allan Hanbury), Technical report, PRIP, TU Wien, 2005.
Bibtex Entry:
@TechReport{TR099,
  author =	 "Lech Szumilas and Allan Hanbury",
  title =	 "Segment Feature Co-ocurrence Based Texture
                  Detection",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-099",
  year =	 "2005",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr99.pdf",
  abstract =	 "This work on texture detection was inspired by the
                  general problem of object recognition in two
                  dimensional still images. One of the crucial
                  challenges associated with the object recognition is
                  selecting and obtaining discriminative features. In
                  analysis of real scenes, like nature or urban
                  places, many objects contain textures, which can be
                  considered as one of the object features. Texture
                  detection may also significantly improve image
                  segmentation, which is one of the tools used for
                  object recognition. This report presents a novel
                  method named Feature Co-ocurence Texture Detector (FCTD)
                  which allows for fully automatic detection of
                  textures common in real scenes, like water in lakes,
                  tiger skin, fields of flowers or tree crowns. The
                  method searches for an alternating color pattern,
                  like for example black and orange stripes covering
                  tiger skin, which is very often present in those
                  textures. The final result is a hierarchy of
                  textures (described by their boundaries and a set of
                  features) detected at multiple precision levels,
                  which can be used for further analysis or texture
                  classification. It is achieved through hierarchical
                  clustering of color pairs related to adjacent image
                  segments, where each segment represents a low color
                  gradient, simple shaped patch of pixels in the
                  image. The results are presented on some images from
                  the Berkeley database.",
}
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