Image Retrieval Based on the Wavelet Features of Interest
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Image Retrieval Based on the Wavelet Features of Interest
Te-Wei Chiang, Tienwei Tsai, and Yo-Ping Huang
2006/10/10
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Outline
1. Introduction 1. Introduction
2. Proposed Image Retrieval System2. Proposed Image Retrieval System
3. Experimental Results3. Experimental Results
4. Conclusions4. Conclusions
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1. Introduction
• Two approaches for image retrieval: – query-by-text (QBT): annotation-based image
retrieval (ABIR)– query-by-example (QBE): content-based
image retrieval (CBIR)
• Standard CBIR techniques can find the images exactly matching the user query only.
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• In QBE, the retrieval of images basically has been done via the similarity between the query image and all candidates on the image database. – Euclidean distance
• Transform type feature extraction techniques– Wavelet, Walsh, Fourier, 2-D moment, DCT, and
Karhunen-Loeve.
• In our approach, the wavelet transform is used to extract low-level texture features.
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• In this paper, we focus on the QbE approach. The user gives an example image similar to the one he/she is looking for.
• Finally, the images in the database with the smallest distance to the query image will be given, ranking according to their similarity.
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System Architecture
1. This system consists of two major modules: • the feature extraction module• the similarity measuring module.
2. In the image database establishing phase:• each image is first transformed from the standard RGB color
space to the YUV space; • then each component (i.e., Y, U, and V) of the image is further
transformed to the wavelet domain.
3. In the image retrieving phase:• the similarity measuring module compares the most significant
wavelet coefficients of the Y, U, and V components of the query image and those of the images in the database and find out good matches.
4. To benefit from the user-machine interaction, a GUI is developed, allowing users to adjust weights for each feature according to their preferences.
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Feature Extraction
• Features are functions of the measurements performed on a class of objects (or patterns) that enable that class to be distinguished from other classes in the same general category.
• Color Space TransformationRGB (Red, Green, and Blue) ->
YUV (Luminance and Chroma channels)
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YUV color space
• YUV is based on the Y primary and chrominance.
– The Y primary was specifically designed to follow the luminous efficiency function of human eyes.
– Chrominance is the difference between a color and a reference white at the same luminance.
• The following equations are used to convert from RGB to YUV spaces:
– Y(x, y) = 0.299 R(x, y) + 0.587 G(x, y) + 0.114 B(x, y),
– U(x, y) = 0.492 (B(x, y) - Y(x, y)), and
– V(x, y) = 0.877 (R(x, y) - Y(x, y)).
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Discrete Wavelet Transform
• Mallat' s pyramid algorithm
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Similarity Measurement
• In our experimental system, we define a measure called the sum of squared differences (SSD) to indicate the degree of distance (or dissimilarity).
• The distance between Q and Xn under the Y component and LL(k) subband can be defined as
m n
kx
kqnYLL
nmYLLnmYLLXQDn
k
2)()( ),(),(),()(
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• The distance between Q and Xn under the component Y can be defined as the weighted combination of LL(k) , LH(k) , HL(k) , HH(k) :
),(),((),( )()()()(
1nYLHYLH
K
knYLLYLLnY XQDwXQDwXQD kkkk
)),(),( )()()()( nYHHYHHnYHLYHLXQDwXQDw kkkk
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• Likewise, the distances between Q and Xn under the component U and V can be defined.
• Then, the overall distance between Q and Xn can be defined as :
),(),(),(),( nVVnUUnYYn XQDwXQDwXQDwXQD
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5. Experimental Results
• 1000 images downloaded from the WBIIS database are used to demonstrate the effectiveness of our system.
• The images are mostly photographic and have various contents, such as natural scenes, animals, insects, building, people, and so on.
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6. Conclusions
• In this paper, a content-based image retrieval method that based on DWT is proposed.
• To achieve QBE, the system compares the most significant wavelet coefficients of the Y, U, and V components of the query image and those of the images in the database and find out good matches by the help of users’ cognition ability.
• Since there is no feature capable of covering all aspects of an image, the discrimination performance is highly dependent on the selection of features and the images involved.
• Since several features are used simultaneously, it is necessary to integrate similarity scores resulting from the matching processes.
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Future Works
• For each type of feature we will continue investigating and improving its ability of describing the image and its performance of similarity measuring.
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Thank You !!!