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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