Content-based Image RetrievalContent-based Image Retrieval

CE 264 Xiaoguang FengMarch 14, 2002

Based on: J. Huang. Color-Spatial Image Indexing and Applications. Ph.D thesis, Cornell Univ., 1998.

Contents

Introduction.Color-histogram vs. Correlogram.Implementations and Results.Conclusion.

Introduction

Motivation of CBIRImage features for CBIR Low Level:

Color Texture Edge/Shape

Object Level: Regions

Color Histogram

The histogram of image I is defined as:For a color Ci , Hci(I) represents the number of pixels of color Ci in image I .

OR:For any pixel in image I, Hci(I) represents the possibility of that pixel is in color Ci.

Most commercial CBIR systems include color histogram as one of the features (e.g., QBIC of IBM).No space information.

Improvement of color histogram

There are several techniques proposed to integrate spatial information with color histograms:

W.Hsu, et al., An integrated color-spatial approach to content-based image retrieval. 3rd ACM Multimedia Conf. Nov 1995.

Smith and Chang, Tools and techniques for color image retrieval, SPIE Proc. 2670, 1996.

Stricker and Dimai, Color indexing with weak spatial constraints, SPIE Proc. 2670, 1996.

Gong, et al., Image indexing and retrieval based on human perceptual color clustering, Proc. 17th IEEE Conf. On Computer Vision and Pattern Recognition, 1998.

Pass and Zabih, Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, 1996.

Park, et al., Models and algorithms for efficient color image indexing. Proc. Of IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997.

Color auto-correlogram

Pick any pixel p1 of color Ci in the image I, at distance k away from p1 pick another pixel p2, what is the probability that p2 is also of color Ci?

P1

P2

k

Red ?

Image: I

Color auto-correlogram

The auto-correlogram of image I for color Ci , distance k:

Integrate both color information and space information.

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iii CCk

C IpIpkppI

Color auto-correlogram

Implementations

Pixel Distance Measures Use D8 distance (also called chessboard distance):

Choose distance k=1,3,5,7 Computation complexity:

Histogram: Correlogram:

|)||,max(|),(8 yyxx qpqpqpD

)*134( 2n

)( 2n

Implementations

Features Distance Measures: D( f(I1) - f(I2) ) is small I1 and I2 are similar. Example: f(a)=1000, f(a’)=1050; f(b)=100,

f(b’)=150 For histogram:

For correlogram:

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|)'()(||'|

mi CC

CCh IhIh

IhIhII

ii

ii

][],[)()(

)()(

)'()(1

|)'()(||'|

dkmik

Ck

C

kC

kC

II

IIII

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Test Environment

300 Color Images: flowers, people, scene, etc.

Test Environment

Image quantized to 512 and 64 colors.First calculate the correlogram and histogram of the 300 images, saved as data file.For each query, calculate the correlogram and histogram of the query image; compare it with the data file; sort the feature distances.The order of the target image in the sorted searching result measures the performance.

Test Results

If there is no differenceno difference between the query and the target images, both methods have good performance.

Query Query ImageImage

(512 colors)(512 colors)

CorrelograCorrelogram methodm method

Histogram Histogram methodmethod

1s1stt

2nd2nd 3r3rdd

4t4thh

5t5thh

1s1stt

2nd2nd 3r3rdd

4t4thh

5t5thh

Test Results

The correlogram method is more stable to color changecolor change than the histogram method.

QuerQueryy

TargetTarget

Correlogram method: 1st

Histogram method: 48th

Test Results

The correlogram method is more stable to large appearance changelarge appearance change than the histogram method.

QuerQueryy

TargetTarget

Correlogram method: 1st

Histogram method: 31th

Test Results

The correlogram method is more stable to contrast & brightness changecontrast & brightness change than the histogram method.

Query Query 11

TargetTarget C: 178th

H: 230th

Query Query 22

Query Query 33

Query Query 44

C: 1st

H: 1st

C: 1st

H: 3rd

C: 5th

H: 18th

Conclusion

The color correlogram describes the global distribution of local spatial correlations of colors.It’s easy to compute.It’s more stable than the color histogram method.

ThanksThanks