Fourth Year Project Presentation

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Fourth Year Project - Presentation

• Project Title
• Content Based Image Retrieval (CBIR)
• Presenters
• Rami Al Tayeche
• Ahmed Khalil
• Supervisor
• Professor Aysegul Cuhadar

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Presentation - Outline

• Introduction
• What is CBIR?
• Applications of CBIR
• Our Approach

• Colour

• Texture

• Shape

• Where We Are
• Conclusion
• Questions and Answers

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Introduction - What is CBIR?

• The term CBIR describes the process of
  retrieving desired images from a large collection
  on the basis of features (such as colour, texture
  and shape) that can be automatically extracted
  from the images themselves.

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Introduction - Reasons for its development

• In many current applications with large image
  databases, traditional methods of image indexing
  have proven to be insufficient.

For example Finger print scanning cannot be
done using a keyword search.
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Introduction - Applications

• Automatic face recognition systems

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Introduction - Applications

• Medical Image Databases

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Introduction - Applications

• Trademark Image Registration

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Our Approach - Image Features

• The image features that we will be focusing on,
  for image retrieval are

• Colour
• Texture
• Shape

Other primitive features not considered are

• Spatial location
• Pixel intensity

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Our Approach - Colour
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Our Approach - Colour Histograms
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Our Approach - Colour Maps
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Our Approach - Minkowski Distance
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Our Approach - Quadratic Distance
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Our Approach - Similarity Matrix
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Our Approach - Implementation
Matlab Code
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Our Approach - Texture
What is Texture?

• Texture is that innate property of all surfaces
  that describes visual patters, and that contain
  important information about the structural
  arrangement of the surface and its relationship
  to the surrounding environment.

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Our Approach - Texture

• Examples

Finger print Texture
Brick Texture
Clouds Texture
Rocks Texture
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Our Approach - Texture Properties

• Co-occurrence matrix
• Based on the orientation and distance between
  image pixels.
• From it we obtain statistics that represent

• Coarseness
• Contrast
• Directionality
• Linelikeness
• Regularity
• Roughness

Texture properties
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Our Approach - Wavelet Texture

• Wavelet Texture
• Textures can be modeled as quasi-periodic
  patterns with spatial/frequency representation.

The wavelet transform transforms the image into a
multi-scale representation with both spatial and
frequency characteristics.
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Our Approach - Tree Algorithm

• Algorithm Tree-Structured Wavelet Transform
• Decompose the image into four sub-images
• Calculate the energy of all decomposed images at
  the same scale, using
• If the energy of a sub-image is significantly
  larger, repeat from step 1.

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Our Approach - Tree Algorithm
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Our Approach - 1st Decomposition
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Our Approach - 2nd Decomposition
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Our Approach - Classification

• Algorithm Euclidean Distance Classification
• Decompose query image.
• Get the energies of the first dominant k
  channels.
• For image i in the database obtain the k
  energies.
• Calculate the Euclidean distance between the two
  sets of energies, using
• Increment i. Repeat from step 3.

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Our Approach - Shape
What is Shape?

• Shape is the characteristic surface configuration
  that outlines an object giving it a definite
  distinctive form.
• Fairly well-defined concept.

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Our Approach - Shape

• Examples

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Our Approach - Shape Features

• Aspect ratio
• Circularity
• Moment invariants
• Sets of consecutive boundary segments

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Our Approach - Shape Extraction

• Techniques under consideration
• Fourier Descriptor
• Moment Invariants
• Directional Histograms

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Where We Are
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Image Database
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Conclusion

• What is CBIR?
• The retrieval of images from a database based on
  content features such as colour, texture and
  shape.
• Reasons for its developments
• Insufficiency in certain applications

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Conclusion

• Applications
• Finger print scanning systems
• Automatic face recognition systems
• Medical image databases
• Trademark image registration

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Conclusion

• Our Approach
• Colour
• Texture
• Shape
• Where we are
• In the phase of understanding and implementing
  shape.