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Content-based Image Retrieval

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Title: Content-based Image Retrieval


1
Content-based Image Retrieval
  • Hai Le
  • Supervisor Sid Ray

2
Content-based Image Retrieval
  • Introduction
  • CBIR fundamentals
  • Overview of the system
  • Relevance feedback
  • Results and Discussion
  • Conclusion

3
Introduction
  • Search and retrieval of image databases
  • Retrieval by query, such as an example image
  • Application areas include, weather
    forecasting, iiiiiiiimedical research, fabric
    design, WWW search iiiiiiiijust to name a few..
  • CBIR systems which are commercially
    iiiiiiiiavailable include IBMs QBIC, Blobworld,
    iiiiiiiiVisualSeek, Virage etc.

4
CBIR Fundamentals
Y
N
5
CBIR Fundamentals
Y
N
6
CBIR Fundamentals
  • User is searching for a specific target item
    from iiiiiiiiia known database
  • User is searching for a class of similar items
  • Query by example image, drawing etc.
  • Exploiting multiple queries to refine searches
  • Use of segmentation or object selection to
    iiiiiiiiisearch for image specifics

7
CBIR Fundamentals
Y
N
8
CBIR Fundamentals
  • Low level image features used as classifiers
  • Most commonly used features include
  • Colour
  • Texture
  • Shape
  • Transform domain features
  • DCT
  • Wavelet filters

9
CBIR Fundamentals
Y
N
10
CBIR Fundamentals
  • Features are represented as a numerical value
  • Similarities between images are determined by
    iiiiiiiiidifferences between values
  • Typically, queries are viewed as points in a
    iiiiiiiiimultidimensional feature space, and the
    iiiiiiiiidistance between points is determined
  • Common distance functions include
  • Euclidean
  • City block
  • Images are indexed in terms of their distance
    iiiiiiiiifrom the query point

11
CBIR Fundamentals
Y
N
12
CBIR Fundamentals
  • Top N most similar images are retrieved
  • Searches can be refined by user feedback
  • Data which is fed back is usually used to
    iiiiiiiidetermine significant features
  • A features influence on a query can be
    iiiiiiiiincreased/decreased by applying a
    weighting iiiiiiiifacture during distance
    calculations
  • Images are re-indexed

13
Overview of the System
  • Query by example image
  • 3 selectable features for querying, with 189
    iiiiiiiisub features
  • Selection between 4 weighting schemes
  • Incorporates user interaction, retrieved
    images iiiiiiiiare selected as either relevant or
    non-relevant

14
Overview of the System
  • Features used in the system
  • Colour histogram
  • Colour moments
  • Edge histogram

15
Overview of the System
  • Colour histogram using linear quantised HSV
    iiiiiiiiicolour space
  • 18 hues (20 degrees separation), 3
    iiiiiiiiisaturations, 3 values, total of 162
    colours
  • Each bin is proportional ie.
  • Colour moments calculated by taking the
    iiiiiiiiiaverage, standard deviation and cube
    root of iiiiiiiiithe third moment, of each of the
    HSV channels

16
Overview of the System
  • Edge histogram is derived by the number of
    iiiiiiiiedge pixels present in the image and the
    iiiiiiiidirection of the edge pixels
  • Sobel edge detector is applied on the image,
    if iiiiiiiithe edge strength surpasses a
    threshold the iiiiiiiidirection is recorded and
    quantised
  • A histogram is generated from all the
    iiiiiiiidirectionality values
  • Each bin represents a sub feature

17
Overview of the System
  • Euclidean metric used to measure distance
  • Features which constitute large values
    iiiiiiiiovershadow those with small values
  • Gaussian normalisation is applied to give
    equal iiiiiiiiemphasis on all features

18
Overview of the System
  • Most similar images are those closest to the
    iiiiiiiiquery image in the feature space
  • Images are indexed by their distance values
  • The top N images are retrieved and displayed

19
Relevance Feedback
  • An image object can be modeled as
  • D is the image data, F fi , features, R
    rij iiiiiiiia set of representation or
    subfeatures, iiiiiiiieg.
    Histogram bins
  • An objects similarity to the query is
    calculated iiiiiiiiby rij and its corresponding
    weight wij and a iiiiiiiisimilarity measure S
  • Weighted Euclidean

20
Relevance Feedback
  • Different queries are more reliant on certain
    iiiiiiiiifeatures than others
  • Varying wijs changes emphasis of features
    iiiiiiiiiduring distance calculation
  • Term weighting using relevance feedback
    iiiiiiiiiinclude
  • Density estimation
  • Support vector machine learning
  • Self-organising maps

21
Relevance Feedback
  • System training data uses binary feedback,
    iiiiiiiiieither positive example or negative
    examples
  • Number of retrieved images is 4 plus original
    iiiiiiiiiquery image
  • Of the 4 examples, images marked as relevant
    iiiiiiiiiare placed in a positive subset,
    unselected iiiiiiiiiimages are placed in a
    negative subset
  • Unretrieved images are left indifferent

22
Relevance Feedback
  • Weighting scheme devised by Rui et. al
  • Form an M x N matrix, M number of positive
    iiiiiiiiimages, N number of features
  • For each column of the matrix calculate the
    iiiiiiiistandard deviation
  • The weight wij is calculated by taking

23
Relevance Feedback
  • Using both positive and negative images
  • If means of rij positive and rij negative are
    iiiiiiiiisimilar, feature is insignificant
  • Using variance

24
Relevance Feedback
  • Positive images are similar because of a
    iiiiiiiiispecific characteristic, negative images
    are iiiiiiiiidifferent because of a number of
    iiiiiiiiicharacteristics
  • Using negative standard deviation

25
Results and Discussion
  • Image database of 200, divided into individual
    iiiiiiiigroups ranging from 4-10 images per group
  • group of 10 images selected for test case
  • each image selected as query, precision and
    iiiiiiiiirecall calculated for each of the 4 term
    iiiiiiiiweighting schemes after 1 iteration
  • Precision and recall averaged over the 10
    iiiiiiiiimages

26
Results and Discussion
27
Results and Discussion
28
Results and Discussion
  • Employing negative examples, showed
    iiiiiiiiiimprovement
  • Type 3 showed slowest degradation
  • Type 4 showed significant improvement

29
Conclusion
  • CBIR system developed incorporating
  • variety of features
  • functional interface
  • user friendly feedback mechanism
  • Improved term weighting scheme over used
    iiiiiiiiin the MARS CBIR system
  • Maintains same simplistic interface as MARS
  • Future work
  • image classification
  • segmentation/local features
  • better feature representation eg. Gabor
    filtering
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