P1258716690OCtfa

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Content-Based Image Retrieval (CBIR) By Victor
Makarenkov Michael Marcovich Noam Shemesh
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What is CBIR

• Content-based image retrieval, a technique which
  uses visual contents to search images from large
  scale image databases according to users'
  interests, has been an active and fast advancing
  research area since the 1990s.
• In our project we concentrated on
  region-histogram features to retrieve the images
  according to an example query image supplied by
  the user.

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• Histogram is a measure used to describe the
  image. In simple words it means the distribution
  of color brightness across the image. The
  brightness values range in 0..255.
• Region based means that the histogram measure is
  not taken globally for the whole image, but
  locally for different image regions. This
  region-histogram features were used as index of
  the image database.
• Weighting for the regions. The closer the part
  to image center the higher its weight in
  similarity measure.

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General CBIR system works according to the
following schema
In our CBIR system we implemented all the parts
except the one of relevance feedback.
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• Visual content description since we using
  histogram of image,
• we transform the file of the image to its bitmap
  representation.
• That means 2D array where each cell contains
• a triple with the RGB brightness values for the
  colors
• Red,
• Green,
• Blue.

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Feature Vector

• Feature vectors In our system, for generality
  purposes we assume that the images are of fixed
  size 200200 pixels. (If not our system converts
  them to that size). We use local histogram
  values. The image is divided into N N square
  areas, and then the histogram computed in each
  area.Each area is of size (200/N)(200/N) pixels
  .Each image is represented with NN length vector
  where each coordinate is the histogram in the
  appropriate area. More precisely and .

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• Similarity comparison for a similarity
  comparison we used the Minkowski distance.
  Minkowski distance between 2 images I and J is
  denoted as while we started our research when
  p2.
• Indexing and retrieval for all images that are
  in the databases the feature vector is
  pre-computed and stored as index in file. When
  retrieval should be made, the image with the
  least Minkowski (most similar images) distance
  between query image and image from database is
  returned.

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• Conclusions
• As we thought at the beginning Histogram is
  quite primitive and insufficient way for CBIR
  purposes. However, with certain image
  characteristics it may be useful, and works well.
  For example on the military ceremony and the
  nature images.
• Another important foundation we made is that one
  of our initial assumptions was wrong. It is that
  dividing the image into many area , does not
  always improve the results of retrieval. In case
  of too many divisions, it degrades the results.
  The reason for that is that while comparing small
  parts, that are corresponding between the images
  and are at fixed place, they can be different.
  But if the same picture can be shifted, and not
  be found! The method is not shift invariant!
• In some cases , small division (4 areas) did
  help. For example on the image of Barcelona it
  moved a similar shifted building up 1 in rank.
• The "Minkowski distance" that was changed several
  times during our experiments did not make
  dramatic changes, but moved some further images
  close when P is enlarged.
• May be used to tuning when similar content image
  exists, but is not ranked top. Enlarging P in
  that case can "push" its rank higher.

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• References
• Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof.
  David Dagan Feng. Content Based Image Retrieval.
• Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof.
  David Dagan Feng . An Effective Region-Based
  Image Retrieval Framework
• Yossi Rubner, Carlo Tomasi, and Leonidas J.
  Guibas The Earth
  Mover's Distance as a Metric for Image Retrieval