text: abstract
  in this paper, a new texture descriptor named "fractional local neighborhood intensity
  pattern" (flnip) has been proposed for content based image retrieval (cbir). it is an
  extension of the local neighborhood intensity pattern (lnip)[1]. flnip calculates the
  relative intensity difference between a particular pixel and the center pixel of a 3×3 window
  by considering the relationship with adjacent neighbors. in this work, the fractional change in
  the local neighborhood involving the adjacent neighbors has been calculated first with respect
  to one of the eight neighbors of the center pixel of a 3×3 window. next, the fractional change
  has been calculated with respect to the center itself. the two values of fractional change are
  next compared to generate a binary bit pattern. both sign and magnitude information are
  encoded in a single descriptor as it deals with the relative change in magnitude in the adjacent
  neighborhood i.e., the comparison of the fractional change. the descriptor is applied on four
  multi-resolution images- one being the raw image and the other three being filtered gaussian
  images obtained by applying gaussian filters of different standard deviations on the raw
  image to signify the importance of exploring texture information at different resolutions in an
  image. the four sets of distances obtained between the query and the target image are then
  combined with a genetic algorithm based approach to improve the retrieval performance by
  minimizing the distance between similar class images. the performance of the method has
  been tested for image retrieval on four databases, including texture image databases such as
  brodatz texture image database and salzburg texture database as well as a biomedical image
  database namely the oasis database and one face database - at&t face database. the
  precision and recall values observed on these databases have been compared with recent
  state-of-art local patterns. the proposed method has shown a significant improvement over
  many other existing methods.