Content-Based Image Retrieval Using Fuzzy Cognition Concepts Presented by Tienwei Tsai Department of...
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![Page 1: Content-Based Image Retrieval Using Fuzzy Cognition Concepts Presented by Tienwei Tsai Department of Computer Science and Engineering Tatung University.](https://reader035.fdocuments.net/reader035/viewer/2022062722/56649f2a5503460f94c4422a/html5/thumbnails/1.jpg)
Content-Based Image Retrieval Using Fuzzy Cognition Concepts
Presented by
Tienwei TsaiDepartment of Computer Science and Engineering
Tatung University2005/9/30
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Outline
1. Introduction 1. Introduction
2. Problem Formulation 2. Problem Formulation
3. Proposed Image Retrieval System3. Proposed Image Retrieval System
4. Experimental Results4. Experimental Results
5. Conclusions5. 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 DCT is used to extract low-level texture features. – the energy compacting property of DCT
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2. Problem Formulation
• Let I be the image database with I := {Xn | n = 1, . . ., N} where Xn is an image represented by a set of features: Xn := {xn m | m = 1, . . ., M}. – N and M are the number of images in the image database and
the number of features, respectively.
• To query the database, the dissimilarity (or distance) measure D(Q, Xn) is calculated for each n as
– dm is the distance function or dissimilarity measure for the mth feature and wm R is the weight of the mth feature.
– Query image Q := {qm | m = 1, …, M}.– For each n, holds. By adjusting the weights wm it is possible to
emphasize properties of different features.
.,,1 ,),(.),(1
N...for nxqdwXQDM
mnmmmmn
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3. The Proposed Image Retrieval System
Figure 1. The proposed system architecture.
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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 CIE Y primary, and also 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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2 Feature Extraction via DCT
• The DCT coefficients F(u, v) of an N×N image represented by f(i, j) can be defined as
where
1
0
1
0
),()()(2
),(N
i
N
j
jifvuN
vuF ),2
)12(cos()
2
)12(cos(
N
vj
N
ui
.1
,021
)(otherwise
wforw
Discrete Cosine Transform
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Characteristics of DCT
• the DC coefficient (i.e. F(0, 0)) represents the average energy of the image;
• all the remaining coefficients contain frequency information which produces a different pattern of image variation; and
• the coefficients of some regions represent some directional information.
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Similarity Measurement
• Distance measure – the sum of absolute differences (SAD): avoid
multiplications. – the sum of squared differences (SSD): exploit
the energy preservation property of DCT
• The distance between qm and xnm under the low frequency block of size k×k :
1
0
1
0
2),(),(),(k
u
k
vxqnmmm vuFvuFxqd
nmm
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Fuzzy Cognition Query
• To benefit from the user-machine interaction, we develop a GUI for fuzzy cognition, allowing users to adjust the weight of each feature more easily according to their preferences.
• Each image is represented by M features. • Three features (i.e., luminance Y,
chrominance U, and chrominance V) are considered for each image.
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4. Experimental Results
• 1000 images downloaded from the WBIIS database are used to demonstrate the effectiveness of our system.
• The user can query by an external image or an image from the database.
• In our experiments, we found that the low frequency DCT coefficients of size 5×5 are enough to make a fair quality of retrieval.
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Figure 2. Retrieved results using a butterfly as the query image and its luminance as the main feature.
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Figure 3. Retrieved results using a butterfly as the query image and emphasizing the weight of its V component.
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Figure 5. Retrieved results using a mountain scene as the query image and Its Y component as the main feature:
(a) the query image; (b) the retrieved images.
(a)
(b)
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Figure 4. Retrieved results using a mountain scene as the query image and Its U component as the main feature:
(a) the query image; (b) the retrieved images.
(a)
(b)
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5. Conclusions
• In this paper, a content-based image retrieval method that exploits fuzzy cognition concepts is proposed.
• To achieve QBE, the system compares the most significant DCT 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 several features are used simultaneously, it is necessary to integrate similarity scores resulting from the matching processes.
• An important part of our system is the implementation of a set of flexible weighting factors for this reason.
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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.
• A long-term aim is combining the semantic annotations and low-level features to improve the retrieval performance.
• For the analysis of complex scenes, the concept that provide a high amount of content understanding enable highly differentiated queries on abstract information level. The concept is worthy of further study to fulfill the demands of integrating semantics into CBIR.
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Thank You !!!