Computer graphics processing and selective visual display system – Computer graphics processing – Attributes
Reexamination Certificate
2000-07-07
2004-10-12
Bella, Matthew C. (Department: 2676)
Computer graphics processing and selective visual display system
Computer graphics processing
Attributes
C382S305000
Reexamination Certificate
active
06803919
ABSTRACT:
TECHNICAL FIELD
The present invention relates to a texture description method for an image, and more particularly, to a method of describing image texture in the frequency domain, in which image signals are converted into those in a frequency domain of the Polar coordinate system to extract texture features. The present invention also relates to a method of texture-based retrieval of images indexed by the texture description method.
BACKGROUND OF THE INVENTION
The texture information of an image is one of the most important visual characteristics of the image and thus, has been studied together with the color information for a substantial period of time. This texture information of the image is usually used as an important low-level visual descriptor in content-based indexing and in abstracting an image or video data. Also, image texture is very important information used for retrieval of a special picture in an electronic album or content-based retrieval in tiles or textiles database.
Until now, feature values have generally been computed in the time domain or in the frequency domain to extract a texture feature of the image. More particularly, the method of extracting the texture features in the frequency domain was known to be suitable for describing image texture information of various types. Extracting texture features in the frequency domain can be done in the Cartesian or the Polar coordinate system.
Conventionally, the Cartesian coordinate system has been widely used in extracting a texture feature in the frequency domain.
A paper entitled “Texture Features For Browsing And Retrieval Of Image Data”, written by B. S. Manjunath and W. Y. Ma is published in “IEEE Transaction on Pattern Analysis and Machine Intelligence”, vol.18, no.8, in August of 1996, addresses a method of dividing the frequency domain of the Cartesian coordinate system based on HVS (Human Visual System) filtering of an image in the respective channels by Gabor filters, and then extracting the average and the standard deviation from the respective channels as texture features of the image.
However, the method of describing image texture is not suitable in the frequency domain of the Cartesian coordinate system for the HVS and leads to poor performance in relevant texture images.
To solve the problem of the image texture description method in frequency domain of the Cartesian coordinate system, a paper on image texture description method in frequency domain of the Polar coordinate system was published, in which the texture information in the frequency domain is computed in the Cartesian coordinate system.
In the paper entitled “Rotation-invariant Texture Classification using a complete Space Frequency Model”, written by B. S. Manjunath and Geoge M. Haley and published in “IEEE Transaction on Pattern Analysis and Machine Intelligence”, vol. 8, no.2, in February of 1999, a method of dividing a frequency space of the Polar coordinate system based on HVS (Hunan Visual System), then extracting 9 feature values using a Gabor filter designed to be suitable for respective channels, and describing the image texture using the extracted feature values of all channels was disclosed.
However, in this method, the same design of a set of Gabor filters is used for extracting different kinds of texture features in the frequency domain.
SUMMARY OF THE INVENTION
The disclosed embodiments of the present invention provide a texture description method in a frequency domain, suitable for HVS, in which image texture features are computed and indexed in a frequency domain.
In accordance with a further embodiment of the present invention, a texture-based retrieval method by using texture features computed in the frequency domain of the Polar coordinate system is provided, in which similar images in different variations, such as different rotations or scales or pixel intensity, are retrieved by comparing a query texture descriptor with a data texture descriptor generated by the texture description method and taking into account such variations thereof.
Also provided is a texture description method in the frequency domain of the Polar coordinate system that includes a first step of generating a frequency layout by partitioning said frequency domain into a set of feature channels; a second step of extracting texture feature values of said image from said respective feature channels; and a third step of constituting a texture descriptor in a vector form by using said texture feature values extracted from said respective feature channels in said frequency layout.
It is preferable that said first step is of generating said frequency layout on the basis of the HVS (Human Visual System), and that said frequency domain in said first step is that of the Cartesian coordinate system or the Polar coordinate system.
It is also preferable that said first step includes a sub-step of generating different frequency layouts for different types of texture features, that is, each texture feature type for its respective frequency layout.
It is further preferable that said first step comprises a sub-step of assigning significance or priority to the respective channels.
Also, it is preferable that said second step include a first sub-step of Radon-transforming said image; a second sub-step of Fourier-transforming said Radon-transformed image; and a third sub-step of extracting said texture feature values of said Fourier-transformed image from said respective feature channels.
It is further preferable that said third sub-step is of extracting at least energy deviation values and/or energy values in said respective feature channels.
Here, it is preferable that a frequency layout for obtaining said energy values and a frequency layout for obtaining said energy deviation value is separately prepared for extracting different types of an image texture, and that said frequency layout for obtaining said energy values partitions said frequency domain at intervals of 2
l
(0≦l<log
2
(N/2)−1) octave in a radial direction and at intervals of ‘180/dividing resolution’ in an angular direction. The frequency layout for obtaining said energy deviation values partitions said frequency domain at the same intervals in a radial direction and at intervals of ‘180/dividing resolution’ in an angular direction.
It is preferable that said third step is of finding out a rotational reference axis of said image by using said image texture information, rotating said frequency layout with reference to said rotational reference axis, and then extracting said image texture descriptor of said image. Here, the rotational reference axis is set to be an axis in a radial direction, in which one of energy, entropy, and a periodical component is most distributed by Radon-transforming said image.
Preferably, the third step is of Fourier-transforming said image to find out a radial reference point, normalizing said Fourier-transformed image with reference to said reference point, and then describing said texture descriptor by using said normalized values of said Fourier-transformed image. Here, the radial reference point is set by determining an arc in which one of energy, entropy, and a periodical component of said Fourier-transformed image apart at the same distance from the origin in said frequency domain is most distributed, and then setting a radius of said founded arc as said radial reference point.
It is preferable that the method of describing image texture in a frequency domain according to the present invention further comprise a fourth step of extracting intensity information of said image to add said intensity information to said texture descriptor.
Also, according to the present invention, a computer readable recording media recording a program for realizing a texture description method in a frequency domain is provided. The program performs a first step of generating a frequency layout by partitioning said frequency domain into a set of feature channels; a second step of extracting texture feature values of said image by Radon-transforming said image in said respectiv
Kim Jin Woong
Kim Mun-churl
Ro Yong Man
You Ki Won
Bella Matthew C.
Blackman Anthony
Electronics and Telecommunications Research Institute
Seed IP Law Group PLLC
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