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Human Color Perception in the HSV Space and its Application in
Histogram Generation for Image Retrieval
A. VadivelDepartment of Computer Science & Engineering
Indian Institute of Technology Kharagpur 721302 India.
CBIR – An Introduction
• Content Based Image Retrieval involves retrieval of images from a database which are “similar” to a query image.
• The similarity metric is usually based on image features such as color, texture, shape, etc.,.
• Some existing CBIR systems:- QBIC, VisualSeek, NeTra, MARS, Blobworld, PicToSeek,SIMPLIcity.
• Color based feature are found to be most effective.
Chose an appropriate Color Space such as RGB, HSV, etc.
To find NN with respect to Color Perception
HSV color representation separates Chrominance and luminance components
Hue
Saturation
Intensity
Developing color based CBIR Applications
View Of HSV Color space
Typical Color Histogram
RGB Color Histogram LSB is truncated
Various BINs
One update for one pixel
A Pixel
When S=0, Hue is undefined
When I=0, Saturation is undefined
Pixel is “True Color”, when H is defined
Pixel is “Gray Color”, when H is Undefined
Updated Here OR Updated Here
TRUE COLOR GRAY COLOR
Useful properties of HSV color space
Histogram
S=0, No Color PerceivedS is increased color perceived
Saturation (S)
I=0, Black Pixel I is increased for S= 1.0
Intensity (I)
Saturation and Intensity values of a pixel determine “True color” pixel or a
“Gray color” pixel.
The variation in perceived color with changes in S and I
min max min max
Fovea centralis(only cones) Very high – resolution color vision
but low light sensitivity
Saturation
Saturation(S)
Color weight
0.250.0
Near Saturation threshold
Transition is gradual
The color is perceived gradually
min
max
Saturation Threshold to determine“True Color and Gray Color”
Color weight = 1 if S>0.25 = 0 if S<=0.25
Color weight is added to the respective BIN’s value of the Color Histogram
wH(S,I) should satisfy the following conditions
(a) wH(S,I) is in the range [0,1]
(b) For S1 > S2, wH(S1,I) > wH(S2,I)
(c) For I1 > I2, wH(S,I1) > wH(S,I2)
(d) wH(S,I) changes slowly with S for high values of I
(e) wH(S,I) changes sharply with S for low values of I
A function WH(S,I)to capture the variation in perceived color with changes in S and I
Justification
High I and low S –All types of cells are excited yet WH
is low due to sharp vision, i.e. simultaneous excitation
of different types of cones in the fovea centralis due to
white light mix leads to loss in color. – captured by condition (d)
Low I and high S– Visual perception is predominantly
by rod cells very low contribution by cone cells.
=> loss of color perception.
- captured by condition (e)
A Typical Choice of WH(S,I).
WI (S,I) = 1 – WH (S,I) WH – True color degree of a
pixel.
WI - Gray color degree of a
pixel.
A Pixel
WH
WI
Here r1 and r2 are constants
with 0.2 and 1.5 as values
+ = 1
0Ifor 0
0Ifor )/255(,
21
rIrsISWH
AND
TRUE COLOR GRAY COLOR
BIN VALUE =
BIN VALUE
+WH
BIN VALUE =
BIN VALUE
+WI
A Pixel
WI
WH
Histogram Generation
Histogram
For each pixel in the imageRead the RGB valueConvert RGB to Hue (H), Saturation (S) and Intensity Value
(I)
Determine wH(S,I) and wI(S,I)
Update histogram as follows:HCPH[Round(H.MULT_FCTR)]=
HCPH[Round(H.MULT_FCTR)]+ wH(S,I)
HCPH[NT+Round(I/DIV_FCTR)]=HCPH[NT+ Round(I/DIV_FCTR)]+wI(S,I)
Histogram Generation Procedure (HCPH)
NT = Round (2MULT_FCTR) +1
Ng = 1DIV_FCTR
Imax
Here NT is total number of True color BIN Ng is total number of Gray color BINS
WEB BASED IMAGE RETRIEVAL SYSTEM
Interested user can visit http://www.imagedb.iitkgp.ernet.in/hcph.php
0
0.5
1
2 5 10 15 20
Nearest Neighbors : Euclidean Distance
Pe
rce
ive
d P
rec
isio
n HSVSN
HSVHD
HCPH
0
0.5
1
2 5 10 15 20
Nearest Neighbors : Manhattan Distance
Pe
rce
ive
d P
rec
isio
n
HSVSN
HSVHD
HCPH
0
0.5
1
2 5 10 15 20
Nearest Neighbors : Vector Cosine Angle Distance
Pe
rce
ive
d P
rec
isio
n HSVSN
HSVHD
HCPH
0
0.5
1
2 5 10 15 20
Nearest Neighbors : Histogram Intersection Distance
Pe
rce
ive
d P
rec
isio
n
HSVSN
HSVHD
HCPH
HSVSN – HSV NormalHSVSD – HSV Hard DecisionHCPH – Human color Perception Histogram
Performance comparison with HSV color histograms.
Variation of Perceived Precision with Nearest Neighbor based on different distance measures
Observation : HCPH scheme leads to higher precision compared to other HSV color histogram for all types of distance measure
(a) (b)
(c) (d)
0
0.5
1
2 5 10 15 20
Nearest Neighbors : RGB Histogram
Per
ceiv
ed P
reci
sion
EU MH
VCAD HI
0
0.5
1
2 5 10 15 20
Nearest Neighbors : JV
Pe
rce
ive
d P
rec
isio
n
EU
HI
0
0.5
1
2 5 10 15 20
Nearest Neighbors : QBIC
Pe
rce
ive
d P
rec
isio
n
EU MH
VCAD HI
0
0.5
1
2 5 10 15 20
Nearest Neighbors : HCPH
Per
ceiv
ed P
reci
sion
EU MH
VCAD HI
(a) (b)
(c) (d)
Performance of color histogram based schemes for different distance measures
EU-Euclidean DistanceMH – Manhattan DistanceVCAD – Vector Cosine Angle DistanceHI- Histogram IntersectionJV- Jain & Vailaya’s scheme
Precision variation with nearest neighbor
Observation : Histogram Intersection distance measure leads to higher precision for most of the color histograms
Comparison of HCPH using Histogram Intersection based distance measure with some existing color
histogram based schemes
0
2
4
6
2 5 10 15 20
Nearest Neighbors : Histogram Intersection Distance
SD o
f Per
ceiv
ed P
reci
sion
RGB JV
QBIC HCPH
JV – Jain & Vailaya’s Method
HCPH- Human color Perception Histogram
Observation: (i) HCPH scheme leads to higher precision for most cases (ii ) HCPH scheme leads to uniformly high correct retrieval -> Lower standard deviation of perceived precision higher Precision
0
0.5
1
2 5 10 15 20
Nearest Neighbors : Histogram Intersection distance
Pe
rce
ive
d P
rec
isio
n
RGB JV
QBIC HCPH
Precision(P) of retrieval of HCPH and some recently proposed CBIR schemes.
Scheme used N=10 N=20 N=50 N=100
Local Fourier Transform (LFT) Quantization (YUV)
27.59 19.76 13.42 9.89
Color Texture Moments (HSV) 32.36 25.16 16.87 12.29
Color Texture Moments (SvcosH, SvsinH, V)
35.81 26.59 18.24 13.40
Multimedia Retrieval Markup Language with Four-Level Relevance Feedback
34.00 30.00 15.48 12.01
2 Systems combSUM Merge 40.00 33.00 20.00 17.48
Color-Spatial Feature (36 Colors) 34.98 29.00 15.99 12.60
Human Color Perception Histogram (HCPH)
46.00 35.90 25.40 19.98
N= No. of images retrieved for query
Average Precision (%)
Conclusion• A Simple color weight function WH of S and I is proposed to
estimate True color degree and Gray color degree of a pixel.
• HCPH scheme has lower histogram dimension (2-D).
• HCPH scheme tries to capture human visual perception of the
color of a pixel for grouping similar pixels in the histogram.
• Histogram Intersection distance metric gives higher precision
compared to other distance metrics.
• Among different color histogram based schemes HCPH leads to higher precision is most cases.
• Standard Deviation of observed Precision is smaller with HCPH scheme.
References
[1] R. Brunelli and O. Mich, “Histograms Analysis for Image Retrieval”, Pattern Recognition 34, pp., 1625-1637, 2001.[2] Y. Deng, B. S. Manjunath, C. Kenney, M. S. Moore and H. Shin, “An Efficient Color Representation for Image Retrieval”, IEEE Transactions on Image Processing, 10, pp., 140-147, 2001. [3] J. C. French, J. V. S. Watson, X. Jin and W. N. Martin, “Integrating Multiple Multi-Channel CBIR Systems”, Inter. Workshop on Multimedia Information Systems (MIS 2003), pp., 85-95, 2003.[4] T. Gevers and H. M. G. Stokman, “Robust Histogram Construction from Color Invariants for Object Recognition”, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 26(1), pp., 113-118, 2004.[5] R. C. Gonzalez and R. E. Woods, “Digital Image Processing “, II Ed. Pearson Education Asia, First Indian Reprint, 2002.[6] Z. Lei, L. Fuzong, and Z. Bo, “A CBIR Method Based Color-Spatial Feature”, Proc. IEEE Region 10th Annual International Conference, pp., 166-169, 1999.[7] H. Mueller, W. Mueller, S. Marchand-Maillet, D. Squire and T. Pun, “ A Web-Based Evaluation System for CBIR”, Third Intl. Workshop on Multimedia Information Retrieval (MIR2001), 2001.[8] T. Ojala, M. Rautiainen, E. Matinmikko and M. Aittola, “Semantic Image Retrieval with HSV Correlograms”, Scandinavian Conference on Image Analysis, pp., 621-627, 2001.[9] H.Yu, M. Li, H J. Zhang and J. Feng, “Color Texture Moments for Content-Based Image Retrieval”, Proc. Int. Conference on Image Processing, Volume III, pp., 929-931, 2002.[10] F. Zhou,J. Feng and Q. Shi, “Texture Feature Based on Local Fourier Transform”, Proc. Int. Conference on Image Processing, pp., 7-10, 2001.
Representation of colors in the histogram.
True Color Components
Gray Color Components
Circular representation of “true colors” and linear representation of “gray colors”.
Pixels when Hue is undefined
Saturation
Pixels when Hue is defined
Weight = 1 if S>0.25 = 0 if S<=0.25
Saturation(S)
Weight
0.250.0
Weight is added to the respective BIN’s value of the Histogram
Saturation Threshold to determine“True Color and Gray Color”min
max