Computer graphics processing and selective visual display system – Computer graphics processing – Attributes
Reexamination Certificate
2000-02-04
2003-09-23
Bella, Matthew C. (Department: 2676)
Computer graphics processing and selective visual display system
Computer graphics processing
Attributes
Reexamination Certificate
active
06624821
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method and apparatus for retrieving an image texture descriptor, and more particularly, to an image texture descriptor retrieving method for retrieving a texture descriptor which is used in searching and browsing an image and describes texture characteristics of the image, and an apparatus thereof.
2. Description of the Related Art
Recently, image texture has emerged as important visual features for searching and browsing a large set of similar image patterns. For example, a conventional texture descriptor for filtering a texture descriptor by a Gabor filter extracts a texture descriptor consisting of coefficients obtained by Gabor filtering. However, although conventional image texture descriptors consist of numerous vectors, it is quite difficult to visually perceive texture structures from the texture descriptor.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a method for retrieving an image texture descriptor which can perceptually capture the texture structures present in an image.
It is another object of the present invention to provide a computer readable storage medium having a computer program stored therein, the program being arranged such that a computer executes the image texture descriptor retrieving method.
It is still another object of the present invention to provide an image texture descriptor retrieving apparatus which performs the image texture descriptor retrieving method.
To achieve the above object, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different orientation coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) selecting candidate data groups among the data groups by a predetermined classification method, (d) determining a plurality of indicators based on orientation coefficients of the filters used in filtering the candidate data groups, and (e) determining the plurality of indicators as the texture descriptor of the image.
The step (a) may further include the step of (a-1) filtering input images using predetermined filters having different scale coefficients, and the step (d) further comprises the step of (d-1) determining a plurality of indicators based on scale coefficients of the filters used in filtering the candidate data groups.
The image texture descriptor retrieving method may further include the step of determining another indicator based on the presence of data groups filtered by filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected candidate data groups.
The image texture descriptor retrieving method may further include the step of calculating the mean and variance of pixels with respect to the filtered images, and obtaining a predetermined vector using the calculated mean and variance.
According to another aspect of the present invention, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different scale coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) determining a plurality of indicators based on scale coefficients of the filters used in filtering data groups selected among the data groups by a predetermined selection method, (d) determining the plurality of indicators as the texture descriptor of the image.
According to still another aspect of the present invention, there is provided a method for retrieving an image texture descriptor for describing texture features of an image, comprising the steps of (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients, (b) projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs, (c) calculating normalized auto-correlation values for each graph, (d) obtaining a local maximums and a local minimum for each normalized auto-correlation value, at which the calculated normalized auto-correlation values form a local peak and a local valley at a predetermined section, (e) defining the average of the local maximums and the average the local minimums as contrast, (f) selecting graphs in which the ratio of the standard deviation to the average of the local maximums is less than or equal to a predetermined threshold as first candidate graphs, (g) determining the type of the second candidate graphs according to the number of graphs filtered by the filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected second candidate graphs, (h) counting the numbers of graphs belonging to the respective types of second candidate graphs and determining predetermined weights of each type of second candidate graphs, (i) calculating the sum of products of the counted numbers of graphs and the determined weights to determine the calculation result value as a first indicator constituting a texture descriptor, (1) determining the orientation coefficients and scale coefficients of the second candidate graphs having the biggest contrast as second through fifth indicators, and (k) determining indicators including the first indicator and the second through fifth indicators as the texture descriptors of the corresponding image.
The image texture descriptor retrieving method may further include the step of calculating the mean and variance of pixels with respect to the filtered images, and obtaining a predetermined vector using the calculated mean and variance, wherein the step (k) includes the step of determining indicators including the first indicator, the second through fifth indicators and the predetermined vector as the texture descriptors of the corresponding image.
The normalized auto-correlation, denoted by NAC(k), is preferably calculated by the following formula:
NAC
(
k
)
=
∑
m
=
k
N
-
1
P
(
m
-
k
)
P
(
m
)
∑
m
=
k
N
-
1
P
2
(
m
-
k
)
∑
m
=
k
N
-
1
P
2
(
m
)
wherein N is a predetermined positive integer, an input image consists of N×N pixels, a pixel position is represented by i, where i is a number from 1 to N, the projection graphs expressed by pixels of the pixel position i is represented by P(i) and k is a number from 1 to N.
The contrast is determined as:
contrast
=
1
M
∑
i
=
1
M
P_magn
(
i
)
-
1
L
∑
i
=
1
L
V_magn
(
i
)
wherein P_magn (i) and V_magn (i) are the local maximums and local minimums determined in the step (d).
In the step (f), the graphs satisfying the following formula are selected as first candidate graphs:
S
d
≤
α
wherein d and S are the average and standard deviation of the local maximums and &agr; is a predetermined threshold.
The step (g) includes the sub-steps of (g-1), if there are one or more graphs having scale or orientation coefficients identical with those of a pertinent candidate graph and one or more graphs having scale or orientation coefficients close to those of the pertinent candidate graph, classifying the pertinent candidate graph as a first type graph, (g-2) if there are one or more graphs having scale or orientation coefficients identical with those of a pertinent candidate graph but there is no graph having scale or orientation coefficients close to those of the pertinent candidate graph, classifying the pertinent candidate graph as a second type graph, and (g-3) if there
Choi Yang-lim
Manjunath Bangalore
Shin Hyun-doo
Wu Peng
Bella Matthew C.
Cunningham G. F.
Samsung Electronics Co,. Ltd.
Sughrue & Mion, PLLC
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