Abstract:
Duplicate images are detected by providing at least two images captured at determinable times; computing an indication of the image content for each image; determining the time of capture of each of the images; and evaluating the indication of image content and the time of capture to determine whether the images are duplicate images. The images are divided into blocks and the indication of image content is computed from a histogram for each block. Thereafter, the step of evaluating the indication of image content and the time of capture comprises comparing one or more blocks of one image, using a histogram intersection metric, to corresponding blocks of another image and using the time difference between capture of the two images to determine whether the images are duplicate images.

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     The present application is related to U.S. application Ser. No. 09/163,618, filed Sep. 30, 1998, by Alexander C. Loui and Eric S. Pavie, and entitled, “A METHOD FOR AUTOMATICALLY CLASSIFYING IMAGES INTO EVENTS” and to U.S. application Ser. No. 09/197,363, filed Nov. 20, 1998, by Alexander C. Loui and Eric S. Pavie, and entitled, “A METHOD FOR AUTOMATICALLY COMPARING CONTENT OF IMAGES FOR CLASSIFICATION INTO EVENTS”. 
     FIELD OF THE INVENTION 
     The invention relates generally to the field of image processing systems that automatically classify pictures by events and the like and, more particularly, to an automatic classification and albuming system that automatically classifies pictures for placement into an album. 
     BACKGROUND OF THE INVENTION 
     Pictorial images are often classified by the particular event, subject, or the like for convenience of retrieving, reviewing, and albuming of the images. This classification is usually achieved by either manually or automatically segmenting the images into appropriate groups. A manual method would involve visually inspecting each image and then placing the image into the appropriate group. An automated albuming method would typically group the images in some automatic manner by color, shape or texture in order to partition the images into groups of similar image characteristics. 
     Although the presently known and utilized methods for partitioning images are satisfactory, there are drawbacks. The manual classification method is obviously time consuming, and the automated albuming method, although theoretically classifying the images into events, is susceptible to misclassification due to the inherent inaccuracies involved with classification by color, shape or texture. In either method, when two pictures are identified as duplicates, one of them is typically removed and will not appear in the resulting album. Since a consumer will not be satisfied if the automatic albuming process removes a picture that should have been in the album, the precision of the duplicate detection algorithm has to be high. Consequently, a need exists for overcoming the above-described drawbacks. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to overcoming one or more of the problems set forth above. Briefly summarized, according to one aspect of the present invention, the invention resides in a method for detecting duplicate images comprising the steps of providing at least two images captured at determinable times; computing an indication of the image content for each image; determining the time of capture of each of the images; and evaluating the indication of image content and the time of capture to determine whether the images are duplicate images. 
     In a further aspect of the invention, the images are divided into blocks and the indication of image content is computed for each block. More specifically the indication of image content is computed from a histogram for each block. Thereafter, the step of evaluating the indication of image content and the time of capture comprises comparing one or more blocks of one image, using a histogram intersection metric, to corresponding blocks of another image and using the time difference between capture of the two images to determine whether the images are duplicate images. Moreover, the step of computing an indication of image content may include dividing each image into blocks, wherein one or more blocks represent a foreground area of the images, and computing an indication of image content in each block and in the foreground areas of each block. 
     Consequently, according to this method image content is analyzed to determine duplicates by the similarity of content of the images and the time of exposure. If the image contents are similar and the time difference between exposures is within a certain threshold, then the images are duplicates. If no time and date information is available, the image content alone can be used. 
     These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings. 
     ADVANTAGEOUS EFFECT OF THE INVENTION 
     When two pictures are identified as duplicates, one of them is removed and will not appear in the album. Since the consumer will not be satisfied if the automatic albuming process removes a picture that should have been in the album, the precision of the duplicate detection algorithm has to be high. The present invention provides for an automatic albuming process with such a high precision rate. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an overview of the present invention; 
         FIG. 2  shows a comparison of Recall versus Precision curves determined for different block sizes, including 2×2, 3×3 and 4×4 block sizes; 
         FIG. 3  shows the results of the comparisons between a 3×3 blocks technique with and without the use of time information; 
         FIG. 4  shows the area covered by a 3×3 arrangement of blocks, including foreground areas represented by blocks  5  and  8 ; 
         FIGS. 5A  and B show examples of color histograms for the central area covered by block  5  as shown in  FIG. 4 ; 
         FIG. 6  shows the comparisons between various 3×3 blocks techniques showing the effect of using (and not using) time information, using the average of the nine histogram intersection values, and using the rules shown with regard to  FIG. 1 ; and 
         FIG. 7  shows a table summarizing the improvements made according to the invention to the duplicate detection method. 
         FIG. 8  is a block diagram of a computer system for implementing the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, the present invention will be described in the preferred embodiment as a software program. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. Given the system and method as shown and described according to the invention in the following materials, software not specifically shown, described or suggested herein that is useful for implementation of the invention is conventional and within the ordinary skill in such arts. Still further, as used herein, computer readable storage medium may comprise, for example; magnetic storage media such as a magnetic disk (such as a floppy disk or a hard drive) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; a solid state electronic storage device such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program. 
     Referring now to  FIG. 1 , there is illustrated a flow diagram illustrating an overview of the present invention. Digitized images are input into a computer system in step S 10 , where a software program will classify them into distinct categories. For instance, the images will be ranked in chronological order by analyzing the time of capture of each image (date may also be used to isolate the time by day, and in the absence of time, date alone can provide a gross estimate of chronological order). The time of capture of each picture may be extracted, for example, from the encoded information on the film strip of the Advanced Photo System (APS) images, or from information available from some digital cameras. Furthermore, each image is divided into N×N blocks (with N=2, 3 or 4 in typical implementations). In the preferred embodiment, the image is divided into 3×3 blocks, as shown in  FIG. 4 . For each block, an indication of image content is computed; more specifically, a color histogram is computed for each block (exemplary histograms of the center block are shown for two similar images in  FIGS. 5A and 5B ). Then each block of one image is compared, using a conventional histogram intersection metric, to the corresponding block of another image. (The histogram intersection metric is described in the aforementioned U.S. Pat. No. 6,606,411 and has the formula 
         Inter   ⁡     (     R   ,   C     )       =         ∑     i   =   1     n     ⁢     min   ⁢           ⁢     (       R   i     ,     C   i       )             ∑     i   =   1     n     ⁢     R   i             
 
where R is the histogram of the reference, C is the histogram of the candidate, and n is the number of bins in the histogram.
 
     At the beginning of the process, two flags IsDup and ForegroundDup are set to true and false, respectively, in a step S 11 . Then, in a step S 12 , the individual histogram intersection value for each block (except for the center block) is checked to determine whether it is higher than a certain threshold T 1 , and the number of intersection values below the threshold T 1  are counted. If the number of blocks with intersection values below the threshold T 1  are found in a step S 14  to be greater than a certain number N, then the flag IsDup is set to false. Next, the foreground average histogram intersection value is computed in step S 15  for a foreground area of the image (where foreground, for purposes of a 3×3 block, is defined as blocks  5  and  8 , as shown in  FIG. 4 ). If the foreground average histogram intersection value is found in a step S 16  to be less than a certain threshold T 2 , then the flag IsDup is set to false. If the foreground average histogram intersection value is found in a step S 18  to be higher than a certain threshold T 3 , then the flag ForegoundDup is set to true. 
     Next, the average of all histogram intersection values for all blocks of each image calculated and checked in a step S 20  to determine whether that average histogram intersection value is greater than a threshold T 4  (with the flag IsDup being true), and further checked in a step S 22  to determine whether that average histogram intersection value is greater than another threshold T 5  (where T 5 &lt;T 4 , and where the flag ForegreoundDup is true). If neither one of these conditions is satisfied, then the decision is made that the image(s) are not duplicate images. If either one of these conditions is satisfied, the decision regarding duplicates is not made at this point. Then, in step S 24  the time difference between the two images is checked to determine whether it is less than a certain threshold T 6 , and further in step S 28  checked to determine whether it is less than another threshold T 8  (where T 6 &lt;T 8 ). If the condition in step  24  is satisfied, or if the condition in step  28  is satisfied in combination with the aforementioned average of all histogram intersection values being found greater in a step  26  than another threshold T 7  (with T 5 &lt;T 4 &lt;T 7 ), then the decision is made that the image(s) are duplicate images. Otherwise, the images are determined not to be duplicate images. 
     In summary, the rules for determining whether an image is a duplicate of another image is as follows:
         a) If S 14  and S 16  are not satisfied, and S 20  and S 24  are satisfied, the pictures are duplicates.   b) If S 14  and S 16  are not satisfied, and S 20 , S 26  and S 28  are satisfied, the pictures are duplicates.   c) If S 18 , S 22 , and S 24  are satisfied the pictures are duplicates.   d) If S 18 , S 22 , S 26  and S 28  are satisfied the pictures are duplicates.   e) Otherwise the pictures are not duplicates.       

     The goal of the duplicate detection algorithm outlined in  FIG. 1  is to identify whether two pictures are so similar that a consumer would only put one of them in an album. A workable definition of a duplicate is as follows: duplicates are defined as two photographs that have the same content and composition, as well as being taken from the same angle and range. Typically, substantially exactly the same content and composition are required to establish a duplicate, although a small variation in angle and/or range may be tolerated. Consequently, the duplicate detection method is trying to recall the “almost identical” pictures, i.e., the two pictures should have almost the same colored pixels at almost the same locations. Obviously, a pixel to pixel comparison will not work well, since there will always exist a small shift; on the other hand, a global color histogram approach will not be accurate enough due to the lack of information about the pixels&#39; locations. The block histogram approach gives semi-local information on the pixel colors and some information of location within the image. The blocks cannot be too big so that the small shift between the images will not affect the accuracy. In the experiment as described below, we decided to divide the image into 3×3 blocks, as shown in  FIG. 4 . 
     EXPERIMENT 
     We have found that date and time information can be very useful in achieving the goal of the duplicate detection algorithm. Indeed, according to the aforementioned definition of duplicates, it would clearly appear that such pictures are usually taken within a short period of time. By setting different thresholds in coordination with the image similarity values, as shown and described in connection with  FIG. 1 , we were able to improve the quality of the duplicate detection significantly. 
     In order to benchmark and verify the algorithm, a third party ground truth database was set up. Four hundred forty three (443) pictures were carefully chosen from the database. The pictures are all duplicates candidates, including a lot of pictures that are obviously not duplicates for a human eye but might be for a machine readable apparatus (same picture but different people, etc.). The database contains about 270 pairs. The third party ground truth has been based on the participation of ten observers. Each observer was given a definition of what are duplicates pictures plus some explanation of how to make a decision. In addition, it was explained to the observers not to put themselves in the place of the consumer, that is, not to develop a like or dislike for the images, but to put themselves in the place of a third party person. Ideally, the interest of the pictures to the observers should not influence their decision whether the images are duplicates. The observers were told that their input will be used for the benchmarking of a duplicate detection system. 
     The output of the duplicate detection method is binary, meaning that a picture is flagged as either a duplicate or not a duplicate. Nevertheless, what the ground truth study provides, for each pair of pictures, is a probability of the pair being duplicates. The metric used to benchmark the method is based on a Recall variable versus a Precision variable, where: 
               Recall   =           #   ⁢           ⁢   correct         #   ⁢           ⁢   correct     +     #   ⁢           ⁢   missed         ⁢           ⁢   and   ⁢           ⁢   Precision     =       #   ⁢           ⁢   correct         #   ⁢           ⁢   correct     +     #   ⁢           ⁢   false   ⁢           ⁢   positive             ⁢                   (Eq.  1)             
 
     The first step was to determine the number of blocks into which the images would be divided. In the aforementioned Ser. No. 09/163,618, a block-based histogram technique was used for event classification but it involved a much larger number of blocks. In the comparative illustration of  FIG. 2 , Recall versus Precision curves were generated for the 2×2 blocks, 3×3 blocks, 4×4 blocks techniques and for a block-based histogram technique involving a larger number of blocks. For the several N×N blocks techniques, an average of the histogram intersections is computed and a threshold is applied. The Recall versus Precision curves are obtained by varying the threshold for each technique. The results of the comparison are shown in  FIG. 2 .  FIG. 2  shows that the new approach with smaller blocks is better than a block-based histogram technique involving a larger number of blocks for the detection of duplicates. It also appears clear that the results of the 3×3 blocks and the 4×4 blocks approaches exceed the results of the 2×2 blocks approach. We decided to use the 3×3 blocks approach for the following reasons: 1) the results are slightly better than with the use of 4×4 blocks; and 2) the 3×3 blocks approach has the advantage of having a middle block, which for the majority of the pictures is likely to contain the main subject. 
     The next step was to determine the influence of the date and time information on the quality of the results. Date and time information turned out to be very relevant information, and enabled us to improve the precision of the results significantly, even though only 57% of the pictures had date and time information. Adaptive thresholds were set up for the time, all optimized for this database. 
       FIG. 3  shows the results of a comparison between a 3×3 blocks technique without the use of time information and a 3×3 blocks technique with the use of time information.  FIG. 3  shows that for reasonable Recall, between 0.65 and 0.75, the precision obtained with date and time included is much higher with an average improvement of 0.065, or 9%. Finally, we optimized the thresholding technique on each block. In addition to the threshold on the average histogram intersections, a threshold on each histogram intersection and a threshold on the average histogram intersections of blocks  5  and  8  have been set up. The blocks  5  and  8  are very likely to contain the main subject. They generally represent the foreground of the image.  FIG. 4  shows the area covered by blocks  5  and  8 . 
       FIGS. 5A and 5B  illustrate how the blocks of each image are compared to each other using color histograms, and more specifically show how the RGB color histograms of the center block of each picture are compared. Even though there is a slight shift between the two images, these figures show that the color histograms for the center block are basically the same. The only noticeable difference is the observable peak in the right part of the histogram of the block of picture B ( FIG. 5B ). This peak corresponds to the small amount of water present in the center block of picture B that is not present in the center block of picture A ( FIG. 5A ) due to the shift. This difference will not be a significant influence on the overall intersection value between these histograms. 
       FIG. 6  shows comparisons between the use of several 3×3 block histogram techniques, one without the use of time information and the others either with the use of time information in combination with the average of the nine histogram intersection values or with time information in combination with the set of rules (or a subset) described above in relation to  FIG. 1 . More specifically, the curve in  FIG. 6  labeled 3×3+time.3, which follows the process shown in  FIG. 1 , shows the improvements realized by the application of the set of rules described above in relation to  FIG. 1 . The best solution was obtained using the following set of thresholds: T 1 0.54; T 2 =0.43; T 3 =0.57; T 4 =0.495; T 5 =0.62; T 6 =2; T 7 =0.62; T 8 =8; N=2. The result achieved is Recall=0.68 and Precision=0.81. This represents an improvement of 0.045 or 6% in Precision at equal Recall compared with the basic technique using time information. 
       FIG. 7  shows a table summarizing the improvements made to the duplicate detection method, in particular showing that the technique according to the invention achieves a significant improvement compared with the block-based histogram technique (an increase of 5.4% in Recall and 23.8% in Precision can be observed). 
     In another embodiment, If no time and date information is available, the block histogram analysis alone provides a method for determining duplicates in an automatic albuming system. Referring to a subset of steps in  FIG. 1 , after inputting the images in step S 10  determine in step S 12  if each individual histogram intersection value (except the center block) is higher than a certain threshold T 1 , and count the number of intersection values below that threshold. Check whether the number of intersection values below that threshold are found in step S 14  to be greater than a certain number N. Compute in step S 15  the average histogram intersection of blocks  5  and  8 . Check in step S 16  whether that average is less than a certain threshold T 2 , and check in step S 18  whether that average is higher than a certain threshold T 3 . Compute the average of the all histogram intersection values and check in step S 20  whether the average of all histogram intersection values is higher than a threshold T 4 . Finally, check in step S 22  whether the average of all histogram intersection values is higher than a threshold T 5  (with T 5 &lt;T 4 ). Then, these various computations are applied as shown in the appropriate parts of  FIG. 1  to determine if the input images are duplicates. 
     While the overall methodology of the invention is described above, the invention can be embodied in any number of different types of systems and executed in any number of different ways, as would be known by one ordinarily skilled in the art. It facilitates understanding to note that the present invention is preferably utilized on any well-known computer system, such as a personal computer. It is also instructive to note that the images may be either directly input into the computer system (for example by a digital camera) or digitized before input into the computer system (for example by scanning). For example, as illustrated in  FIG. 8 , a typical hardware configuration of an information handling/computer system useful in implementing the invention preferably has at least one processor or central processing unit (CPU)  100 . The CPU  100  is interconnected via a system bus  101  to a random access memory (RAM)  102 , a read-only memory (ROM)  103 , an input/output (I/O) adapter  104  (for connecting peripheral devices such as disk units  105  and tape drives  106  to the bus  101 ), a communication adapter  107  (for connecting an information handling system to a data processing network, such as the Internet), a user interface adapter  108  (for connecting peripherals  109 ,  110 ,  111  such as a keyboard, mouse, digital image input unit (e.g., a scanner or a camera), microphone speaker and/or other user interface device to the bus  101 ), a printer  112  and a display adapter  113  (for connecting the bus  101  to a display device  114 ). The invention could be implemented using the structure shown in  FIG. 8  by including the inventive method within a computer program stored, e.g., on the storage device  105 . Such a computer program would act on a time series of image frames supplied through the interface adapter  108  or through the network connection  107  in order to detect duplicates. The system would then automatically produce the desired digital image frame output (without duplicates) on the display  114 , the printer  112  or sent back to the network  107 . 
     The invention has been described with reference to a preferred embodiment. However, it will be appreciated that variations and modifications can be effected by a person of ordinary skill in the art without departing from the scope of the invention. 
     PARTS LIST 
     
         
           100  CPU 
           101  bus 
           102  RAM 
           103  ROM 
           104  I/O adapter 
           105  disk unit 
           106  tape drive 
           107  communication adapter 
           108  interface adapter 
           109  keyboard 
           110  mouse 
           111  digital image input unit 
           112  printer 
           113  display adapter 
           114  display device