QBIC (Query by Image Content)

• Currently supported features: query by color, texture, simple shape, and keywords

• It is being used in IBM's Multimedia Manager in OS/2, and in DB2 Extenders

Query by example using color histograms

• Example image:
  

• One of the colour histograms for the example image is shown in Fig. 13.1.
  

  Colour Histogram of Image

• Store a histogram vector for each image in the database,

  compare images using mean squared difference,

  return images sorted by this metric.

• We'll find the images displayed in Fig. 13.2.
  

  Result of QBIC Search

• The method has the following advantages:
  • Very, very fast
  • Easily implemented
  • Invariant to small changes in camera angle (and sometimes large ones!)

• The method has the following disadvantages:
  • Sensitive to illumination changes.
  • Sensitive to different levels of gamma correction.
  • Doesn't account for location of colour. The images in Fig. 13.3 two are equivalent
    

    Equivalent Colour in QBIC

  • Could use three additional colour edge images and their histograms to solve this problem.

• Color constancy problems: two otherwise equivalent scenes will have different histograms depending on the colour of the incident light.

Possible variations on the theme

• Use other metrics: mean squared difference, mean absolute difference, cosine of angle, etc.

• Use dominant colours - k bins in the histogram that have larger counts

• Use other colour spaces, and/or uneven weights (e.g. 8 bits for green, 5 red, 3 blue) -> perceptual weighting

• Also, colour histogramming can be augmented by other simple statistical measures, e.g. mean brightness, moments, etc.

Other Queries in QBIC

• By texture: coarseness ("edgeness"), contrast, and edge directionality.

• By sketching: Sketch a shape and use it for a pattern match (the current demo version doesn't work well).

• By keywords: Often there's a set of keywords and other textual data associated with a picture.