Patent Publication Number: US-6985628-B2

Title: Image type classification using edge features

Description:
BACKGROUND OF THE INVENTION 
     The invention relates to image processing. It finds particular application in conjunction with classification of images between natural pictures and synthetic graphics, and will be described with particular reference thereto. However, it is to be appreciated that the invention is also amenable to other like applications. 
     During the past several decades, products and services such as TVs, video monitors, photography, motion pictures, copying devices, magazines, brochures, newspapers, etc. have steadily evolved from monochrome to color With the increasing use of color products and services, there is a growing demand for “brighter” and more “colorful” colors in several applications. Due to this growing demand, display and printing of color imagery that is visually pleasing has become a very important topic. In a typical color copier application, the goal is to render the scanned document in such a way that it is most pleasing to the user 
     Natural pictures differ from synthetic graphics in many aspects, both in terms of visual perception and image statistics. Synthetic graphics are featured with smooth regions separated by sharp edges. On the contrary, natural pictures are often noisier and the region boundaries are less prominent. In processing scanned images, it is sometime beneficial to distinguish images from different origins (e.g., synthetic graphics or natural pictures), however, the origin or “type” information about a scanned image is usually unavailable. The “type” information is extracted from the scanned image. This “type” information is then used in further processing of the images. High-level image classification can be achieved by analysis of low-level image attributes geared for the particular classes. Coloring schemes (e.g., gamut-mapping or filtering algorithms) are tailored for specific types of images to obtain quality reproduction. Once an image has been identified as a synthetic graphic image, further identification of image characteristics can be used to fine-tune the coloring schemes for more appealing reproductions The most prominent characteristics of a synthetic graphic image include patches or areas of the image with uniform color and areas with uniformly changing colors. These areas of uniformly changing color are called sweeps. 
     Picture/graphic classifiers have been developed to differentiate between a natural picture image and a synthetic graphic image by analyzing low-level image statistics. For example, U.S. Pat. No. 5,767,978 to Revankar et al. discloses an adaptable image segmentation system for differentially rendering black and white and/or color images using a plurality of imaging techniques. An image is segmented according to classes of regions that may be rendered according to the same imaging techniques Image regions may be rendered according to a three-class system (such as traditional text, graphic, and picture systems), or according to more than three image classes. In addition, two image classes may be required to render high quality draft or final output images. The image characteristics that may be rendered differently from class to class may include half toning, colorization and other image attributes. 
     Synthetic graphics are typically generated using a limited number of colors, usually containing a few areas of uniform colors On the other hand, natural pictures are more noisy, containing smoothly varying colors. A picture/graphic classifier can analyze the colors to distinguish between natural picture images and synthetic graphic images. 
     Synthetic graphic images contain several areas of uniform color, lines drawings, text, and have very sharp, prominent, long edges. On the other hand, natural pictures are very noisy and contain short broken edges. A picture/graphic classifier can analyze statistics based on edges to distinguish between natural picture images and synthetic graphic images. 
     Classifiers that can be used to solve a certain classification problem include statistical, structural, neural networks, fuzzy logic, and machine learning classifiers. Several of these classifiers are available in public domain and commercial packages. However, no single classifier seems to be highly successful in dealing with complex real world problems. Each classifier has its own weaknesses and strengths. 
     The invention contemplates new and improved methods for classifying images that overcome the above-referenced problems and others 
     SUMMARY OF THE INVENTION 
     A method and system for classifying images between natural pictures and synthetic graphics is provided. In embodiments of the invention, a picture/graphic classification method and system implements edge features for image classification of natural pictures and synthetic graphics. In another embodiment of the invention, a picture/graphic combination classification method and system using edge features, color discreteness features, and SGLD texture features is used to classify images between natural picture and synthetic graphic classes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps The drawings are for purposes of illustrating embodiments of the invention and are not to be construed as limiting the invention. 
         FIG. 1  is a flowchart of an image classification process using SGLD texture features in an embodiment of the invention; 
         FIG. 2  is a more detailed flowchart of the SGLD matrix initialization and construction process portion of the flowchart in  FIG. 1 , 
         FIG. 3  is a flowchart of an image classification process using one-dimensional (1-D) color discreteness features in an embodiment of the invention; 
         FIG. 4  is a flowchart of an image classification process using two-dimensional (2-D) color discreteness features in an embodiment of the invention; 
         FIG. 5  is a flowchart of an image classification process using three-dimensional (3-D) color discreteness features in an embodiment of the invention, 
         FIG. 6  is a flowchart of an image classification process using a combination of 1-D color discreteness features, 2-D color discreteness features, and 3-D color discreteness features in an embodiment of the invention; 
         FIG. 7  is a flowchart of an image classification process using edge features in an embodiment of the invention; 
         FIG. 8  is a flowchart of an image classification process using a combination of SGLD texture features, color discreteness features, and edge features in an embodiment of the invention; and, 
         FIG. 9  is a block diagram of an image processing system using a “binary” image classification process (i e., classification of images between natural picture or synthetic graphic classes). 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Spatial gray-level dependence (SGLD) techniques for image analysis are well known. SGLD feature extraction creates a 2-D histogram that measures first and second-order statistics of an image. These features are captured in SGLD matrices. This was originally proposed for texture analysis of multi-level images Additionally, since texture features distinguish natural pictures from synthetic graphics, SGLD techniques can be applied to picture/graphic classification of images A picture/graphic classifier can be created with algorithms that analyze the texture features captured in SGLD matrices Using the SGLD texture features, the classifier works to determine whether a scanned image is a natural picture or synthetic graphic Furthermore, in color images, the luminance component typically contains enough information to determine the origin of the image. Therefore, an SGLD matrix that captures the luminance component of an image and a picture/graphic classifier using the luminance component from the matrix in a classification algorithm can determine whether the image is a natural picture or synthetic graphic. 
     With reference to  FIG. 1 , a flowchart of an image classification process  100  using SGLD texture features in an embodiment of the invention is shown. Generally, the classification process filters an input image to smooth out halftones, builds an SGLD matrix from the smoothed image, extracts texture features from the matrix, and performs an algorithm to determine whether the image is a natural picture or synthetic graphic based on one or more of the texture features. 
     More specifically, the process  100  begins with an input image  102  The image is processed using a low-pass filter  104  (e.g, a W×W averaging filter) to smooth the luminance component and reduce any halftone noise The SGLD matrix is basically a GL×GL 2-D histogram, where GL is the number of gray levels (e.g, 256) The SGLD matrix is generated by first performing an initialization (e.g., set to zero)  106 . Next, the SGLD matrix is built from the smoothed image  108 . The SGLD matrix is a 2-D histogram corresponding to certain characteristics of the pixels in the input image For each pixel (m,n) in the smoothed image, a neighboring value is calculated using the following logic and equations.
 
if| x ( m,n+d )− x ( m,n )|&gt;| x ( m+d,n )− x ( m,n )|
 
then  y ( m,n )= x ( m,n+d ),
 
otherwise  y ( m,n )= x ( m+d,n ),  (1),
 
where x(m,n) is the smoothed pixel value at (m,n), (m,n+d) and (m+d,n) are vertical and horizontal neighbors, respectively, and d is a fixed integer (typically one or two)
 
     With reference to  FIG. 2 , a flowchart of an embodiment of the SGLD matrix initialization and construction process is shown. The initialization step  106  sets the SGLD matrix to zero and sets a pixel counter (N) to zero  154 . The SGLD matrix is constructed from a low-pass filtered image  152  provided by the low-pass filter  104  Construction of the SGLD matrix begins by getting a pixel (m,n)  156  from the filtered image. A neighboring value for the pixel (m,n) is calculated using the algorithm in equation (1) If |x(m,n+d)−x(m,n)|&gt;|x(m+d,n)−x(m,n)|  158 , then y(m,n)=x(m,n+d)  160  Otherwise, y(m,n)=x(m+d,n)  162  As is apparent, if pixel (m,n) is in a flat area where x(m,n) is equal to y(m,n), the entry [x(m,n), y(m,n)] is on the diagonal. On the other hand, if (m,n) is on an edge, the difference between x(m,n) and y(m,n) will be significant, and [x(m,n), y(m,n)] will be far away from the diagonal. 
     The entry [x(m,n), y(m,n)] in the SGLD matrix is then increased by one and the pixel counter (N) is increased by one Next, a check is made to determine if the calculation was for the last pixel  166  of the input image If so, SGLD matrix construction is complete and the SGLD matrix is ready for feature extraction  168  Otherwise, the next pixel is retrieved  156  from the input image. 
     For the matrix, the neighboring pixels in synthetic graphic images are expected to be either correlated or very different. In other words, for synthetic graphic images, SGLD matrix entries are usually either on the diagonal or far away from the diagonal This is because most pixels are either at the flat regions or on the edges On the other hand, pixels of natural pictures are not expected to have many abrupt changes Accordingly, masses are expected to be concentrated at the entries that are near the diagonal for natural picture images. This shows the noisy nature of the natural picture images. 
     Returning to  FIG. 1 , many features (e.g., variance, bias, skewness, fitness) can be extracted from the SGLD matrix to classify the input image between natural picture and synthetic graphic classes. The features can be implemented individually or combined in various methods (e.g., linear combination). Once the SGLD matrix is built, a feature or combination of features is selected for extraction  110  and processed using feature algorithms. For example, a first feature algorithm measures variance (V) (i e, the second-order moment around the diagonal)  112  and is defined as
 
 V=Σ   |n−m|Δ   s ( m,n )( m−n ) 2   /N   (2),
 
where s(m,n) is the (m,n)-th entry of the SGLD matrix, Δ is an integer parameter typically between 1 and 16 and;
 
 N=Σ   |n−m|&gt;Δ   s ( m,n )  (3)
 
     As the summation is over all (m,n) such that |m−n|&gt;Δ, all the pixels in the flat regions are ignored. For synthetic graphic images, the remaining pixels are on the edges, while for natural picture images, both pixels in the noisy regions and pixels on the edges are included Variance (V) is typically larger for synthetic graphic images than for natural picture images 
     The second feature algorithm measures average bias (B)  114  and is defined as:
 
 B=Σ   |n−m|&gt;Δ   s ( m,n )[ n−μ ( m )] 2   /N   (4),
 
where μ(m) is the mean of s(m,n) for a fixed m. For a given m, the distribution of s(m,n) is roughly symmetrical about the diagonal for natural picture images, as noise typically has a zero mean symmetrical distribution. As a result B is usually small for natural picture images. For synthetic graphic images, s(m,n) is usually unsymmetrical and B is large.
 
     The third feature algorithm measures skewness (S)  116  and is defined as: 
               S   =     skewness   =       ∑     n   =   0       GL   -   1       ⁢                  ∑     m   =   0       GL   -   1       ⁢            n   -   m          ⁢     (     n   -   m     )     ⁢     s   ⁡     (     m   ,   n     )                      1           2               ∑     m   =   0       GL   -   1       ⁢            n   -   m          ⁢     s   ⁡     (     m   ,   n     )             ⁢       c   ⁡     (   n   )       /   C             ,           (   5   )                 where   ⁢     :       ⁢     
     ⁢       c   ⁡     (   n   )       =         ∑     m   =   0       GL   -   1       ⁢       s   ⁡     (     m   ,   n     )       ⁢           ⁢   and   ⁢           ⁢   C       =       ∑     n   =   0       GL   -   1       ⁢       c   ⁡     (   n   )       .                   (   6   )             
 
     The fourth feature algorithm measures fitness (F)  118  and is defined to be: 
               F   =     fitness   =         ∑     n   =   0       GL   -   1       ⁢         (     n   -   m     )     2     ⁢     s   ⁡     (     m   ,   n     )             σ   2           ,           (   7   )             
 
where σ is defined such that 
                 ∑     d   =   0     σ     ⁢     [       s   ⁡     (     m   ,     m   +   d       )       +     s   ⁡     (     m   ,     m   -   d       )         ]       =     0   ⁢           ⁢   6   ×   C             (   8   )             
 
     The image type decision  120  compares the result of the feature algorithm(s) to previously selected low and high thresholds (i.e., T L  and T H , respectively) depending on the algorithm(s) and combinations selected. If the result of the feature algorithm(s) is below the low threshold (T L ), the image is classified as a natural picture  122 . If the result exceeds the high threshold (T H ), the classification is synthetic graphic  126 . Obviously, if the behavior of a particular feature is converse to this logic, the decision logic can be easily reversed to accommodate. If the result of the feature algorithm(s) is equal to or between the low and high thresholds, the class of the image cannot be determined (i.e., indeterminate  124 ) from the feature or combination of features selected. It is understood that a number of other alternatives are possible For example, a result equal to a particular threshold can be said to be determinative of the image class, rather than indeterminate Also, in certain circumstances the low and high threshold can be equal. 
     With reference to  FIG. 3 , a flowchart of an image classification process  200  using 1-D color discreteness features in an embodiment of the invention is shown. The process  200  begins with an input image  202 . First, the input image is transformed into a color space  204 , in which the classification is performed Although CIELUV space is used in the embodiment being described, many other color spaces can also be used. Next, the image is smoothed using an averaging filter  206  to remove any noise due to halftones. For example, a 4×4 filter was used successfully Next, a 1-D color histogram is computed for a color channel (i.e., luminance (L), U, and V)  208 , any combination of two 1-D color histograms may be computed, or all three 1-D color histograms may be computed, depending on the algorithm or combination of algorithms to be performed for classification. 
     Next, the histogram(s) may be pre-filtered using a low-pass filter, for example an averaging filter  209  to smooth out artificially boosted probabilities due to the size of the sample. The averaging filter, for example, works particularly well for images with a relatively small sample size that lead to a sparse distribution over the histogram Next, the histogram(s) may be pre-adjusted by applying an F(H) function to histogram entries with high bin counts representing large areas  210  in the image in the same color. Predetermined thresholds define high bin counts and determine whether or not the F(H) function is applied to the histogram bin. The F(H) function is a monotonically decreasing function (e.g., cube root) and under-weighs the histogram counts for the large areas. This under-weighing, for example, works particularly well to correct a bias to incorrectly classify pictures with large flat regions (e g, sky, paper background) as synthetic graphic images 
     The L, U, and/or V histograms are normalized  211  by the number of pixels in the image. The color representation scheme is invariant under rotation and translation of the input image and the normalization provides scale invariance. If I(i) is the histogram of an image, where the index i represents a histogram bin, then the normalized histogram H is defined as follows: 
               H   ⁡     (   i   )       =       I   ⁡     (   i   )           ∑     i   =   0       GL   -   1       ⁢     I   ⁡     (   i   )                   (   9   )             
 
     Since synthetic graphics are generated using a limited number of colors, synthetic graphic images usually are comprised of a few areas of uniform color Hence, the color histograms for a synthetic graphic image usually contain several sharp peaks. On the other hand, natural pictures usually contain more colors with smoothly varying transitions. Hence, natural pictures are more noisy and produce histograms containing fewer and smoother peaks. This difference in the histograms is captured in color discreteness algorithms for each color channel (i.e., R — L algorithm  212 , R — U algorithm  214 , and R — V algorithm  216 ). The color discreteness algorithms are defined as follows: 
               R_L   =       ∑     x   =   1       GL   -   1       ⁢            H_L   ⁢     (   x   )       -     H_L   ⁢     (     x   -   1     )                  ,           (   10   )                 R_U   =       ∑     x   =   1       GL   -   1       ⁢            H_U   ⁢     (   x   )       -     H_U   ⁢     (     x   -   1     )                  ,           (   11   )                 R_V   =       ∑     x   =   1       GL   -   1       ⁢            H_V   ⁢     (   x   )       -     H_V   ⁢     (     x   -   1     )                  ,           (   12   )             
 
where GL is the number of bins in the H — L, H — U, and H — V color histograms The algorithms in equations (10)–(12) are derived from the 1-D color discreteness algorithm for any color space, defined for color channel s of a generic color space as follows.
 
 R   s   =Σ|H   s ( x )− H   s ( x− 1)|  (13).
 
     The image type decision  218  compares the results of the 1-D color discreteness algorithms (i.e.,  212 ,  214 , or  216 ) selected for performance to previously selected thresholds (e g, low threshold (T L ) and high threshold (T H )) If the result of the selected 1-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules Otherwise, the class of the image cannot be determined (i.e., indeterminate  124 ) by the 1-D color discreteness feature (i.e., R — L, R — U, or R — V). Alternatively, the classifier may use all three 1-D color discreteness features in any sequence, any combination of two features in any sequence, or any one feature (as described above) 
     With reference to  FIG. 4 , a flowchart of an image classification process  300  using 2-D color discreteness features in an embodiment of the invention is shown. Like the process  200  using 1-D color discreteness features, the process  300  begins with an input image  202 , the input image is transformed into a color space  204 , and the image is smoothed using an averaging filter  206  Again, although CIELUV space is used in the embodiment being described, many other color spaces can also be used Next, a 2-D color histogram is computed for two color channels (i.e., LU, LV, or UV)  308 , any combination of two 2-D color histograms may be computed, or all three 2-D color histograms may be computed, depending on the algorithm or combination of algorithms to be performed for classification. 
     Next, like the process  200  using 1-D color discreteness features, the 2-D histogram(s) may be pre-filtered using an averaging filter  209  and may be pre-adjusted by applying an F(H) function to histogram entries representing large areas  210  Pre-filtering is important for the 2-D histogram(s), as they are typically sparse Next, the LU, LV, and/or UV histograms are normalized  311  by the number of pixels in the image using equation (9). 
     Next, like the process  200  using 1-D color discreteness features, the difference between histograms of natural picture images and synthetic graphic images is captured in 2-D color discreteness algorithms (i.e., R — LU algorithm  312 , R — LV algorithm  314 , and R — UV algorithm  316 ) The 2-D color discreteness algorithms for the LUV color space are defined as follows. 
               R_LU   =       ∑     x   =   1       GL   -   1       ⁢       ∑     y   =   1       GL   -   1       ⁢     (                    H_LU   ⁢     (     x   ,   y     )       -     H_LU   ⁢     (       x   -   1     ,   y     )              ⁢           +                        H_LU   ⁢     (     x   ,   y     )       -     H_LU   ⁢     (     x   ,     y   -   1       )              ⁢                   ⁢           )           ,           (   14   )                 R_LV   =       ∑     x   =   1       GL   -   1       ⁢       ∑     v   =   1       GL   -   1       ⁢     (                    H_LV   ⁢     (     x   ,   y     )       -     H_LV   ⁢     (       x   -   1     ,   y     )              ⁢           +                        H_LV   ⁢     (     x   ,   y     )       -     H_LV   ⁢     (     x   ,     y   -   1       )              ⁢                   )           ,           (   15   )                 R_UV   =       ∑     x   =   1       GL   -   1       ⁢       ∑     y   =   1       GL   -   1       ⁢     (                    H_UV   ⁢     (     x   ,   y     )       -     H_UV   ⁢     (       x   -   1     ,   y     )              ⁢           +                        H_UV   ⁢     (     x   ,   y     )       -     H_UV   ⁢     (     x   ,     y   -   1       )         ⁢                ⁢                   )           ,           (   16   )             
 
where GL is the number of bins for each component in the H — LU, H — LV, and H — UV color histograms. The algorithms in equations (14)–(16) are derived from the 2-D color discreteness algorithm for any color space, defined for color channel s and color channel t of a generic color space as follows
 
 R   st =Σ(| H   st ( x,y )− H   st ( x− 1, y )|+| H   st ( x,y )− H   st ( x,y −1)|)  (17)
 
     The image type decision  318  compares the results of the 2-D color discreteness algorithm (i.e.,  312 ,  314 , or  316 ) selected for performance to previously selected thresholds (e.g., low threshold (T L ) and high threshold (T H )). If the result of a 2-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules Otherwise, the class of the image cannot be determined (i.e., indeterminate  124 ) by the 2-D color discreteness feature (i.e., R — LU, R — LV, or R — UV). Alternatively, the classifier may use all three 2-D color discreteness features in any sequence, any combination of two features in any sequence, or any one feature (as described above). 
     With reference to  FIG. 5 , a flowchart of an image classification process  400  using 3-D color discreteness features in an embodiment of the invention is shown. Like the process  200  using 1-D color discreteness features and the process  300  using 2-D color discreteness features, the process  400  begins with an input image  202 , the input image is transformed into a color space  204 , and the image is smoothed using an averaging filter  206 . Again, although CIELUV space is used in the embodiment being described, many other color spaces can also be used. Next, a 3-D color histogram is computed for the three color channels (i.e., LUV)  408 . 
     Next, like the process  200  using 1-D color discreteness features and the process  300  using 2-D color discreteness features, the 3-D histogram may be pre-filtered using an averaging filter  209  and may be pre-adjusted by applying an F(H) function to histogram entries representing large areas  210 . Pre-filtering is important for the 3-D histogram, as it is typically sparse. Next, the LUV histogram is normalized  411  by the number of pixels in the image using equation (9). 
     Next, like the process  200  using 1-D color discreteness features and the process  300  using 2-D color discreteness features, the difference between histograms of natural picture images and synthetic graphic images is captured in a 3-D color discreteness algorithm (i.e., R — LUV algorithm  414 ) The 3-D color discreteness algorithm is defined as follows: 
               R_LUV   =       ∑     x   =   1       GL   -   1       ⁢       ∑     y   =   1       GL   -   1       ⁢       ∑     z   =   1       GL   -   1       ⁢     (                      H_LUV   ⁢     (     x   ,   y   ,   z     )       -     H_LUV   ⁢     (       x   -   1     ,   y   ,   z     )         ⁢                +     ⁢                                  H_LUV   ⁢     (     x   ,   y   ,   z     )       -     H_LU   ⁢     (     x   ,     y   -   1     ,   z     )         ⁢                ⁢           +     ⁢                                H_LUV   ⁢     (     x   ,   y   ,   z     )       -     H_LU   ⁢     (     x   ,   y   ,     z   -   1       )         ⁢                ⁢                   ⁢           )             ,           (   18   )             
         where GL is the number of bins for each component in the H — LUV color histogram. The algorithm in equation (18) is derived from the 3-D color discreteness algorithm for any color space, defined for a generic color space as follows 
             R   =     ∑       (                    H   ⁡     (     x   ,   y   ,   z     )       -     H   ⁡     (       x   -   1     ,   y   ,   z     )              +                        H   ⁡     (     x   ,   y   ,   z     )       -     H   ⁡     (     x   ,     y   -   1     ,   z     )              +                        H   ⁡     (     x   ,   y   ,   z     )       -     H   ⁡     (     x   ,   y   ,     z   -   1       )              ⁢                   )     .               (   19   )             
       

     The image type decision  418  compares the results of the 3-D color discreteness algorithm (i.e.,  414 ) to previously selected thresholds (e.g., low threshold (T L ) and high threshold (T H )). If the result of a 3-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules Otherwise, the class of the image cannot be determined (i e., indeterminate  124 ) by the 3-D color discreteness feature (i e, R — LUV). 
     With reference to  FIG. 6 , a flowchart of an image classification process  500  using a combination of 1-D color discreteness features, 2-D color discreteness features, and 3-D color discreteness features in an embodiment of the invention is shown Notably, this color discreteness combination classifier includes features of the three classifiers using 1-D color discreteness features, 2-D color discreteness features, and 3-D color discreteness features discussed above By combining the three color discreteness classifiers, performance may be improved over an individual color discreteness classifier. The color discreteness combination classifier can take advantage of reduced processing overhead by making an early classification, for example, using the 1-D color discreteness features where the classification is more obvious Conversely, the color discreteness combination classifier can take advantage of increased precision by continuing to analyze the statistics of the input image using 2-D color discreteness features and 3-D color discreteness features. 
     Like the individual processes  200 ,  300 ,  400 , the process  500  begins with an input image  202 , the input image is transformed into a color space  204 , and the image is smoothed using an averaging filter  206 . Again, although CIELUV space is used in the embodiment being described, many other color spaces can also be used 
     The process  500  continues by performing steps  208  through  216  of the 1-D color discreteness classifier  508  shown in  FIG. 3  The image type decision  510  compares the results of the 1-D color discreteness algorithms (i.e.,  212 ,  214 , or  216 ) selected for performance to previously selected thresholds (e.g., low threshold (T L ) and high threshold (T H )). If the result of the selected 1-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules Otherwise, the class of the image cannot be determined (i e., indeterminate  512 ) by the 1-D color discreteness feature (i e, R — L, R — U, or R — V). Alternatively, the 1-D color discreteness classifier may use all three 1-D color discreteness features in any sequence, any combination of two features in any sequence, or any one feature during step  508   
     If the result of the 1-D color discreteness classifier is indeterminate  512 , the process  500  continues by performing steps  308  through  316  of the 2-D color discreteness classifier  514  shown in  FIG. 4  The image type decision  516  compares the results of the 2-D color discreteness algorithms (i.e.,  312 ,  314 , or  316 ) selected for performance to previously selected thresholds (e.g., low threshold (T L ) and high threshold (T H )). If the result of a 2-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules. Otherwise, the class of the image cannot be determined (i e, indeterminate  518 ) by the 2-D color discreteness feature (i e, R — LU, R — LV, or R — UV). Alternatively, the 2-D color discreteness classifier may use all three 2-D color discreteness features in any sequence, any combination of two features in any sequence, or any one feature during step  514   
     If the result of the 2-D color discreteness classifier is indeterminate  518 , the process  500  continues by performing steps  408  through  414  of the 3-D color discreteness classifier  520  shown in  FIG. 5 . The image type decision  522  compares the results of the 3-D color discreteness algorithm (i.e.,  414 ) to previously selected thresholds (e.g., low threshold (T L ) and high threshold (T H )) If the result of a 3-D color discreteness algorithm is above T H  or below T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules. Otherwise, the class of the image cannot be determined (i.e., indeterminate  124 ) by the 3-D color discreteness feature (i.e, R — LUV). 
     Alternatively, the color discreteness combination classifier may perform the 1-D color discreteness classifier, the 2-D color discreteness classifier, and 3-D color discreteness classifier in any sequence and may perform all three color discreteness classifiers or any two color discreteness classifiers before making an image type decision. 
     With reference to  FIG. 7 , a flowchart of an image classification process  600  using edge features in an embodiment of the invention is shown. The process  600  begins with an input image  602 . First, edges of color areas in the image are detected  604  using a standard Canny edge detector and an edge map image is created. The parameters identified for the edge detector were determined empirically. Deviations that produce suitable results are also contemplated. Next, the edges in the edge map image are connected  606  (e.g., using a standard 8-connected component algorithm) The average number of pixels per connected edge (P/E) in the edge map image is used as a feature  608  The algorithm for this edge feature is defined as 
               P   /   E     =           No   .           ⁢   of     ⁢           ⁢   Edge   ⁢           ⁢   Pixels         No   .           ⁢   of     ⁢           ⁢   Connected   ⁢           ⁢   Edges       .             (   20   )             
 
     Typically, synthetic graphic images have fewer connected edges, but each connected edge includes a large number of pixels. On the other hand, pictures have a lot more connected edges, but usually very few pixels in each connected edge This feature is particularly accurate for high values. In other words, if the value of P/E is high, it is almost certain that the image is synthetic graphic image However, if the value of P/E is low, the classification of the image is less certain (e.g, indeterminate) This is because the P/E value may be low for synthetic graphic images that have low frequency halftones or certain types of backgrounds. Accordingly, the image type decision  610  compares the result of the P/E feature algorithm to a previously selected high threshold (i e., T H ) If the result exceeds the high threshold (T H ), the classification is synthetic graphic  126  Otherwise, the class of the image cannot be determined (i e, indeterminate  124 ). 
     The orientation of edges (EO) in the edge map image is also used as a feature  609 . As shown in  FIG. 7 , the connected edges  606  in the edge map image are used to compute an edge orientation histogram  607 . For example, the Hough Transform may be used to compute the edge orientation histogram The Hough Transform is a well-known method for finding lines. A detailed description of the Hough Transform can be found in “Digital Picture Processing”, by Azriel Rosenfeld and Avinash C. Kak, (Academic Press, Inc, 1982) Vol. 2, pp 121–126. The Hough Transform converts an edge map image into a 2-D histogram with one dimension being the line orientation and the other being the line intercept A Hough Transform entry HT (x,y) represents the length of a line that has an orientation of x and an intercept of y. The edge orientation histogram H(x)  607  can be obtained by manipulating the HT (x,y) histogram as follows:
 
 H ( x )=(Σ HT ( x,y ) 2 ) 1/2   (21),
 
where the summation is over all y values.
 
     The edge orientation (EO) algorithm  609  is performed on the edge orientation histogram H(x)  607  as follows.
 
 EO=−ΣH ( x ) log H ( x )  (22)
 
     It has been observed that synthetic graphic images often contain many long straight edges that are oriented in the same direction, typically horizontal and vertical directions, while the edges in natural picture images are typically shorter and oriented more randomly. If many of the edge pixels have a common orientation, the resulting histogram will show one or more spikes. The result of the edge orientation algorithm (i.e., entropy measure) for a spiky histogram is a low number Accordingly, the image type decision  612  compares the result of the EO feature algorithm  609  to a previously selected low threshold (i.e., T L ). If the result is less than the low threshold (T L ), the classification is synthetic graphic  126 . Otherwise, the class of the image cannot be determined (i.e., indeterminate  124 ). 
     Furthermore, it is understood that additional edge features may be used for classification of images between natural picture and synthetic graphic classes Any combination of edge features can also be used by the classifier in any sequence. 
     With reference to  FIG. 8 , a flowchart of an image classification process  700  using a combination of SGLD texture features, color discreteness features, and edge features in an embodiment of the invention is shown Notably, this image classifier combines all the features of the three types of classifiers discussed above, including 1-D, 2-D, and 3-D features in regard to color discreteness By combining SGLD texture, color discreteness, and/or edge features in one classifier, performance may be improved over classifiers using a single feature. 
     The process  700  begins with an input image  702 . Next, the features are extracted from the input image  704 . Feature extraction includes compiling SGLD texture features  706  (e.g., variance (V), bias (B), skewness (S), fitness (F)), color discreteness features  708 , including 1-D color discreteness features, (e g, R — L, R — U, R — V), 2-D color discreteness features, (e.g., R — LU, R — LV, R — UV), and 3-D color discreteness features, (e.g., R — LUV), and edge features  710  (e.g., P/E, EO). 
     The SGLD texture features are extracted by performing steps  104 – 110  of the process  100  depicted in  FIG. 1 . Similarly, the 1-D color discreteness features are extracted by performing steps  204 – 211  of the process  200  depicted in  FIG. 3  Likewise, the 2-D color discreteness features are extracted by performing steps  204 – 311  of the process  300  depicted in  FIG. 4 . Similarly, the 3-D color discreteness features are extracted by performing steps  204 – 411  of the process  400  depicted in  FIG. 5  Likewise, the edge features are extracted by performing steps  604 – 607  of process  600  depicted in  FIG. 7 . 
     Next, the feature algorithms are performed  716  The SGLD texture feature algorithms are performed by accomplishing steps  112 – 118  of the process  100  depicted in  FIG. 1  Similarly, the 1-D color discreteness feature algorithms are performed by accomplishing steps  212 – 216  of the process  200  depicted in  FIG. 3  Likewise, the 2-D color discreteness feature algorithms are performed by accomplishing steps  312 – 316  of the process  300  depicted in  FIG. 4 . Similarly, the 3-D color discreteness feature algorithm is performed by accomplishing step  414  of the process  400  depicted in  FIG. 5 . Likewise, the edge feature algorithms are performed by accomplishing steps  608  and  609  of the process  600  depicted in  FIG. 7 . 
     Finally, the image type decision  718  compares the results of the feature algorithms (e.g., V, B, S, F, R — L, R — U, R — V, R — LU, R — LV, R — UV, R — LUV, P/E, EO) to previously selected thresholds (e.g., low thresholds (T L ) and/or high thresholds (T H )) associated with each feature. If the result of any feature algorithm is above the associated T H  or below the associated T L , the image is classified as either a synthetic graphic  126  or natural picture  122  according to predetermined rules Otherwise, the class of the image cannot be determined (i.e., indeterminate  124 ) by the combination classifier. The combination classifier may also incorporate logic to resolve conflicting results, particularly where one individual classifier is indeterminate  124  and another individual classifier classifies the image as either a synthetic graphic  126  or natural picture  122 . 
     The embodiment described and shown in  FIG. 8  uses all features available to the combination classifier and performs feature extraction  704  in a parallel fashion, performs feature algorithms  716  in a parallel fashion, and then makes a combination image type decision  718 . Alternatively, the combination classifier may use all features available to it in any sequence. In other words, feature extraction  704 , feature algorithms  716 , and image type decisions  718  can be performed for individual features, in a serial fashion, similar to that described above for the color discreteness combination classifier shown in  FIG. 6 . Further alternatives, using a serial-parallel combination in any order with any combination of any two or more features in parallel legs of such a combination classifier are also contemplated. Many other alternative combination classifiers are also available by using any combination of two or more of the features available to the combination classifier in parallel fashion, serial fashion, or serial-parallel fashion. 
     With reference to  FIG. 9 , a block diagram of an image segmentation system  800  using a “binary” image classification process (i e, classification of images between natural picture or synthetic graphic classes) is shown The picture/graphic classifiers (i.e.,  100 ,  200 ,  300 ,  400 ,  500 ,  600 ,  700 ) of  FIGS. 1–8  are “binary” classifiers and could be implemented in such a system  800 . As described above for  FIGS. 1–8 , an input image  802  is provided to a feature extractor  804 . The feature extractor  804  extracts pertinent characteristics (i.e., features) based on the parameters required by algorithms of the binary classifier  806  The binary classifier  806  exercises algorithms designed to classify the input image between a natural picture or a synthetic graphic image (e.g., [0, 1] where 0 indicates natural picture and 1 indicates synthetic graphic). This binary classification result is provided to a switch  808 . The switch  808  receives the input image  802  and switches it between natural picture processing  810  and synthetic graphic processing  812 , depending on the binary classification result 
     Natural picture processing  810  processes the image in a manner tailored to maximize the quality of natural picture images (e g., gamut mapping) Similarly, synthetic graphic processing  812  is tailored to maximizes the quality of synthetic graphic images (e.g., filtering). If the input image is classified as a picture, the input image  802  is switched to natural picture processing  810  and a picture output  814  is produced. Alternatively, if the image is classified as a synthetic graphic, the input image  802  is switched to synthetic graphic processing  812  and a synthetic graphic output  816  is produced. In the event that the binary classifier  806  cannot determine the class of the input image, one of the processes (e.g., natural picture processing  810 ) may be selected by default. 
     The invention has been described above with reference to various embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.