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A smart content-based image retrieval
system based on color and
texture feature
指導教授:李育強
報告者 :楊智雁
日期
:2010/09/21
Contents lists available at ScienceDirect,
Image and Vision Computing 27 (2009)
南台科技大學
資訊工程系
Outline
2
1
Introduction
2
Proposed features
3
Image retrieval system
4
Experiments
5
Conclusion
1. Introduction
Many contemporary scholars have been very much
devoted to the design of image databases
When applied to large-scale image databases ,these
features become troublesome and time-consuming
Color co-occurrence matrix (CCM)
3
1. Introduction (c.)
Difference between the pixels of a scan pattern
(DBPSP) to improve texture description
Propose color histogram for K-mean (CHKM) to
clearly describe color features
CCM, DBPSP, and CHKM are able to effectively
describe various properties of an image
4
2. Proposed features
1.Color co-occurrence matrix (CCM)
This paper proposes a color co-occurrence matrix
(CCM) to represent the traversal of adjacent pixel
color difference in an image
5
2. Proposed features (c.)
6
2. Proposed features (c.)
M i (u , v)
m[u , v]
Ni
7
6
6
N i M i (u , v)
u 0 v 0
2. Proposed features (c.)
2.Difference between pixels of scan pattern
(DBPSP)
The CCM feature not the complexity of textures
8
2. Proposed features (c.)
1 ( x, y ) p1 p 2 p 2 p3 p3 p 4
2 ( x, y ) p1 p3 p3 p 2 p 2 p 4
3 ( x, y ) p1 p3 p3 p 4 p 4 p 2
4 ( x, y ) p1 p 2 p 2 p 4 p 4 p3
5 ( x, y ) p1 p 4 p 4 p3 p3 p 2
6 ( x, y ) p1 p 4 p 4 p 2 p 2 p3
1
fi
Ni
9
Ni
i
j ( x, y )
j 1
2. Proposed features (c.)
3. Color histogram for K-mean
The pixels of all database images are categorized into
K clusters using the K-mean clustering algorithm
This paper evenly divides colors into 16 groups
Nk
g
N
k
10
3. Image retrieval system
CCM
49
k 1
DBPSP
m m
mkq mkd
6
k 1
CHKM
16
k 1
q
k
d
k
f kq f kd
q
d
fk fk
g kq g kd
q
d
gk gk
CTCHRIRS w1 CCM w2 DBPSP w3 CHKM
11
4. Experiments
12
資料庫中有10000筆資料,和美食有關的有500篇
。使用者輸入美食的關鍵字後,回傳的文章有
4000篇,其中有400篇是和美食有關的。
Precision = 400 / 4000 = 10%
Recall = 400 / 500 = 80%
13
5. Conclusion
In this paper, three image features, namely CCM,
DBPSP, and CHKM, are presented to characterize a
color image for image retrieval
CCM and DBPSP can effectively describe texture
distribution
CHKM can describe color features of the pixels with
similar colors in an image
14
5. Conclusion (c.)
The experimental results indicate that, in most cases,
the proposed system can significantly outperform
Huang’s and Jhanwar’s methods
The proposed image retrieval system has a high
detection rate when applied to various image
databases
15
南台科技大學
資訊工程系