Content-Based Image Retrieval Using Grey Relational Analysis Dept. of Computer Engineering Tatung University Presenter: Tienwei Tsai (???)

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Content-Based Image Retrieval Using Grey
Relational Analysis Dept. of Computer
EngineeringTatung University Presenter
Tienwei Tsai (???)
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Outline

• Introduction
• Feature Extraction
• Color Space Transformation
• Discrete Cosine Transform (DCT)
• Similarity Measurement
• Problem Description
• Grey Relational Analysis
• Grey Similarity Measurement
• Experimental Results
• Conclusions

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Introduction (1/4)

• Query-by-text has two major drawbacks
• Each image in the database has to be described by
  keywords, which is extremely time consuming.
• The expressive power of keywords is limited and
  cannot be exhaustive.
• Retrieving images according to the image contents
  is called content-based image retrieval (CBIR).
• Image objects are typically represented in CBIR
• Low-level features including color, texture, and
  shape, etc.
• Usually stored in the form of a feature vector.

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Introduction (2/4)

• Searching for k most-similar images to a query
  image
• Comparing the feature vectors of all the images
  in the database with that of the query image
  using some pre-selected similarity measure.
• This requires linear time with respect to the
  size of the database and quickly becomes
  impractical for large databases.
• A compact representation of the features needs to
  be carefully chosen before applying any of these
  indexing techniques

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Introduction (3/4)

• We intend to achieve an acceptable retrieval
  result using
• A content descriptor formed by only one single
  feature, the Y-component in YUV color space.
• The first 16 DCT coefficients transformed from
  the Y-component of an image.
• The run-time complexity of this method is small,
  since the length of the feature vector is small.

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Introduction (4/4)

• While retrieving images, the query results are
  uncertain to some extent therefore, retrieval
  process can be regarded as a grey system.
• The Gray Relational Analysis (GRA) method
• Describes the relationship between a main factor
  and all the other factors in an uncertainly and
  incompletely grey environment.
• We develop a CBIR matching technique based on the
  GRA method.
• This method performs a high efficiency of
  retrieval with an acceptable accuracy.

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Feature Extraction (1/5)

• CBIR often comprises both indexing and retrieval.
  
• Feature extraction finds out the suitable
  properties of interest and converting them into
  mathematical feature vectors.
• The feature vectors are stored into the database
  along with original images.
• It is a critical phase in CBIR, because the
  following retrieval process depends upon the
  correctness of the selected features.

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Feature Extraction (2/5)

• DCT is one of the best filters for feature
  extraction in the spatial frequency domain.
• The steps of the proposed feature extraction
• Transform a RGB image into the YUV color space.
• Calculate the low frequency DCT coefficients for
  Y-component.

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Feature Extraction (3/5)

• YUV color space
• Y luminance (or brightness)
• Y(x, y) 0.299 R(x, y)0.587 G(x, y)0.114 B(x,
  y)
• U blue chrominance
• U(x, y) 0.492(B(x, y)-Y(x, y))
• V red chrominance
• V(x, y) 0.877(R(x, y)-Y(x, y))
• Psycho-perceptual studies have shown that the
  human brain perceives images largely based on
  their luminance value, and only secondarily based
  on their color information

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Feature Extraction (4/5)

• Discrete Cosine Transform
• DCT coefficients are generated for a NN image on
  a pixel by pixel basis. The NN DCT coefficients
  thus give the nature of textual energy for each
  pixel.

where x(i, j) is the pixel value at the (i, j)
coordinate position in the image, C(u, v) is DCT
domain representation of x(i, j), and
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Feature Extraction (5/5)

• The coefficients with small u and v correspond to
  low frequency components.
• For most images, much of the signal energy lies
  at low frequencies.
• We generate a feature vector formed with low
  frequency DCT terms transformed from the
  Y-component of an image .
• Our experiments have shown that the use of a
  block size of 4x4 low frequency coefficients
  performs well from a viewpoint of retrieval
  quality.

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Similarity Measurement (1/5)

• The user query feature extraction will first
  convert a user's exemplary image into a
  mathematical feature vector compatible with the
  feature vectors stored in the database.
• Similarity matching is typically defined by a
  distance function, e.g. Euclidean distance.
• Fewer items in a feature vector will lead to a
  faster matching at the expense of accuracy.

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Similarity Measurement (2/5)

• Grey system theory can perform grey relational
  analysis (GRA) for data sequences.
• GRA can be used to determine the relational grade
  between the reference and each sequence in the
  given set.
• The best comparative one can be found by further
  sorting the resultant relational grades .

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Similarity Measurement (3/5)

• The GRA method can be described as follows.
• Suppose the reference sequence is
• Define the grey relational coefficient between
  y0 and xi at the kth item as follows

where n stands for the number of items in a data
sequence. Denote the m sequences to be compared
by
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Similarity Measurement (4/5)
where

i 1, m, k 1, , n, and
is a distinguishing coefficient to control the
resolution between and
It is set to 0.5 in this study.
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Similarity Measurement (5/5)

• The grey relational grade for an entire sequence
• The larger the grey relational grade
  is, the higher the relative similarity of xi to
  y0.

represents to the degree of
similarity between the sequence xi and the
reference sequence y0.
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Experimental Results

• Two image databases are used in the system
• 1000 images downloaded from the WBIIS.
• 1000 images downloaded from the test database
  used in SIMPLIcity paper.
• No pre-processing was done on the images.
• We conducted three queries using the proposed GRA
  method in this system.
• There had been very little systematic evaluation
  of CBIR, particularly in situations in which the
  user does not have a target image in mind. .

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Figure 1. The main screen of the proposed system
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The first query

Figure 2. Retrieved results using the
GRA method. An image of a flower was given
in the first query. We obtained eight correct
answers (related to flowers) in the top 10
images.
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The second query

Figure 3. Retrieved results using the
GRA method. An image of the sunset was
given in the second query. We obtained three
correct answers (related to sunsets) in the top
10 images.
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The third query

Figure 4. Retrieved results using the
GRA method. An image of some people was
given in the third query. We obtained nine
correct answers (related to people) in the top 10
images.
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Conclusions (1/2)

• A CBIR method based on the GRA method is
  proposed.
• Each image is first transformed from the standard
  RGB color space to the YUV space then Y
  component of the image is further transformed to
  its DCT domain.
• The low frequency DCT coefficients are applied to
  extract low-level features from the images due to
  its superiority in energy compacting.

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Conclusions (2/2)

• The retrieval process is treated as a grey
  system.
• We define a measure using GRA method to indicate
  the degree of similarity between the query image
  and candidate images in the database.
• Those images with the largest value of grey
  relational grade are chosen as the query results.
• The experimental system demonstrated the
  efficiency and effectiveness of our proposed
  method.