JP2007122111A - Importance setting apparatus and method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To allow a user to recognize a category in which images classified thereto is important, with respect to the images hierarchized and classified into a plurality of categories, without imposing a burden on the user. <P>SOLUTION: An importance setting section 32, for each category acquired by classifying the plurality of images into the plurality of categories, groups similar images to generate similar image groups. Then, according to the number of similar image groups in each category and/or the number of images included the similar image group, sets the importance of each category. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an importance setting apparatus and method for setting importance of a plurality of categories obtained by classifying a plurality of images, and a program for causing a computer to execute the importance setting method.

  With the widespread use of digital cameras and scanners, anyone can easily acquire digital images. In addition, images are also organized on a personal computer. In this case, images are often organized by classifying images for each event that has occurred to the user. For example, when you go on a trip to Europe, images acquired in France, the United Kingdom, Spain, etc. are classified into categories for each country, and for images classified in the French category, going out in Paris, Nice, etc. Classify images into more detailed categories for each location. For images classified in Paris, the images are classified into finer categories such as the Eiffel Tower, Arc de Triomphe and Notre Dame.

  If the images are hierarchized and classified into a plurality of categories in this manner, the images divided for each event can be referred to when referring to the images later, so that the images can be easily organized.

  On the other hand, the operation of classifying images into hierarchies is very troublesome for the user because it is necessary to perform the operation while looking at the image and following the user's memory.

  For this reason, various methods for automatically classifying images have been proposed. For example, the images are arranged on the time axis based on the shooting date / time information indicating the shooting date / time given to the images, and the images are classified into a plurality of categories according to the difference in shooting time difference between two images having adjacent shooting times. A classification method has been proposed (see Patent Document 1). Also, a method for classifying images into categories according to the user's schedule with reference to schedule information representing the user's schedule (see Patent Document 2), and calculating feature values from a plurality of images, A method of classifying images into a plurality of categories based on the relationship between them has also been proposed (see Patent Document 3).

By using such a method, the user does not need to classify the image by himself / herself, so that the burden on the user when arranging the images can be reduced.
JP 2000-112997 A JP 2003-109773 A JP 2001-256244 A

  However, when the images are automatically classified as described above, it is impossible to know which category of the image is important for the user until the contents of the image classified for each category are confirmed.

  SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and allows an image classified into a plurality of categories to be identified in which category the image is important without placing a burden on the user. With the goal.

The importance setting device according to the present invention includes grouping means for grouping similar images and generating similar image groups for each category obtained by classifying a plurality of images into a plurality of categories,
Setting means for setting the importance of each category in accordance with the number of the similar image groups in each category and / or the number of images included in the similar image group. .

  In the importance level setting apparatus according to the present invention, the setting means includes at least one piece of information on the similar type in the images included in the similar image group, the number of similar images according to the similar type, and the degree of similarity. Based on the above, it may be a means for further setting the importance of the images included in the similar image group.

  “Similar type” means that when any one of the images included in the similar image group is used as a reference image, how the image for determining similarity is changed with respect to the reference image. This is a parameter indicating whether or not they can be matched. Specifically, translation, enlargement / reduction, rotation, brightness, color, and the like can be used. That is, if the similar type is parallel translation, the image for judging similarity can be matched with the reference image by parallel translation, and if the similar type is enlarged or reduced, the image for judging similarity is enlarged or reduced. This means that it can be matched with the reference image.

  The “number of similar images according to similar types” is the number of images included in each small group when images are classified into small groups for each similar type.

  The “similarity” represents the degree of change in similar types, and specifically, the degree of change such as translation, enlargement / reduction, rotation, brightness, and color is used. it can.

Further, in the importance setting device according to the present invention, display means for performing various displays,
You may make it further provide the display control means which displays the importance of each said category and / or the importance of the said image on the said display means so that visual recognition is possible.

  In the case where the importance level of the image is displayed in a visually recognizable manner, the setting means is a means for selecting a predetermined number of images with the highest importance level as a representative image of the plurality of images, and the display control means is the representative image It is good also as a means to display an image on the said display means.

The importance setting method according to the present invention generates similar image groups by grouping similar images for each category obtained by classifying a plurality of images into a plurality of categories,
The importance of each category is set according to the number of the similar image groups in each category and / or the number of images included in the similar image group.

  In addition, you may provide as a program for making a computer perform the importance setting method by this invention.

  When shooting using a digital camera or the like, important scenes cannot be recovered if shooting fails, so the number of shots increases, and as a result, similar images increase. In addition, there is a demand for capturing an image of a larger number of scenes and storing an image of the event as the event is more important.

  According to the present invention, since the importance of each category is set according to the number of similar image groups in each category and / or the number of images included in the similar image group, without burdening the user, It will be understood to which category the image is important.

  Further, the importance level of the images included in the similar image group is further set based on at least one information of the similar type in the images included in the similar image group, the number of similar images according to the similar type, and the similarity degree. This makes it possible to know which images included in a category are important.

  Further, by displaying the importance levels set in a plurality of categories in a visually recognizable manner, the user can recognize the importance levels for each category at a glance by looking at the display result.

  Also, by displaying the importance set in multiple categories and the importance set in the image in a visible manner, the user can recognize the importance for each category and the importance of the image at a glance by looking at the display result. can do.

  In this case, by selecting and displaying a predetermined number of images with the highest importance as the representative images of a plurality of images, the user can easily know which image has the higher importance. Further, since the representative image is often important for the user, displaying the representative image can guide the user to use the representative image such as a print order.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a schematic block diagram showing the configuration of an importance setting device according to an embodiment of the present invention. As shown in FIG. 1, an importance setting device 1 according to an embodiment of the present invention performs various controls such as image data recording control and display control, and also controls a CPU 12 that controls each part of the device 1, and a CPU 12. A system memory 14 composed of a program to be operated, viewer software for viewing images, a ROM in which various constants are recorded, and a RAM serving as a work area when the CPU 12 executes processing, and various instructions. And an input unit 16 including a keyboard and a mouse for performing the above and a display unit 18 including a liquid crystal monitor for performing various displays.

  The importance setting device 1 is represented by a card slot 20 for reading image data from the memory card 2 on which image data is recorded and recording image data on the memory card 2, and the image data is represented by JPEG. A compression / decompression unit 22 that compresses or decompresses compressed image data; a hard disk 24 that records image data and various programs executed by the CPU 12; a memory that controls the system memory 14, the card slot 20, and the hard disk 24 The control part 26 and the display control part 28 which controls the display of the display part 18 are provided.

  The importance setting device 1 includes an image classification unit 30 that classifies images stored in the hard disk 24 into a plurality of categories, and an importance setting unit 32 that sets the importance of each category.

  The image classification unit 30 classifies a plurality of images stored in the hard disk 24 into a plurality of categories by using the method described in Patent Document 1, for example. Hereinafter, image classification will be described. Here, a case will be described in which a plurality of images acquired by photographing when a user goes on a trip to France is classified. FIG. 2 is a diagram showing a time table for one day of a user's trip to France. As shown in FIG. 2, it is assumed that the user visited Paris on a French trip and then moved to Nice by bus. In Paris, the Arc de Triomphe, the Eiffel Tower and Notre Dame are visited. Assume that the next day you visited the Burgundy region.

Here, if the user goes on a trip to France and then goes on a different trip, there will be a difference of one day or more between the French trip and the other trip. In addition, if you visit Paris and Nice on one day and visit the Burgundy region on the next day, you will have more than an hour difference between your visit to Paris and Nice and your visit to Burgundy. In the case of traveling, there will be no difference for 1 hour between visiting Paris, in the bus, and visiting Nice, but it will be more than 30 minutes. The Arc de Triomphe, Eiffel Tower and Notre Dame are visited in Paris, so the visit time will not be different for 30 minutes, but will be more than 10 minutes.

  Therefore, when shooting is performed collectively at the place of visit, when the shooting time interval is less than 10 minutes between the images with adjacent shooting times, the number of images is plotted on the time axis, assuming that the shooting times are the same. The relationship is as shown in FIG. That is, as shown in FIG. 3, in the relationship between time and the number of images, it can be seen that a plurality of images are acquired at a certain time for each visited site.

  The image classification unit 30 classifies the images by hierarchizing them into a plurality of categories using this relationship. That is, referring to the relationship shown in FIG. 3, first, an image group having a shooting time interval of one day or more is classified as one category. In this case, all images acquired in the France trip are classified as one category. Next, for the classified images, an image group having an imaging time interval of 1 hour or more is classified as one category. In this case, the image group acquired in the Paris and Nice visits and the image group acquired in the Burgundy region visit are further classified as one category.

  Further, with respect to the classified images, an image group whose shooting time interval is 30 minutes or more and less than 1 hour is classified as one category. In this case, in the Paris and Nice visit categories, the image groups acquired in the Paris visit, in the bus, and in the Nice visit are further classified as one category. Furthermore, an image group whose shooting time interval is 10 minutes or more and less than 30 minutes is classified as one category. In this case, in the Paris visit category, the image groups acquired in the Arc de Triomphe visit, the Eiffel Tower visit, and the Notre Dame visit are further classified as one category.

  Then, one folder is created for each category, and the classified images are stored in folders corresponding to the respective categories, thereby completing the image classification.

  FIG. 4 shows the folder structure after classification. Here, the folder structure when the images of the first day in France travel are classified will be described. As shown in FIG. 4, a folder F1 (French travel folder) corresponding to a French travel category for storing images acquired in a French travel is in the top layer, and is classified into a Paris category in the lower layer of the folder F1. A folder F1-1 corresponding to the Paris category and a folder F1-corresponding to the category moving on the bus for storing the images, images classified in the category moving on the bus, and images classified in the Nice category, respectively. Folder F1-3 corresponding to category 2 and Nice is created.

  Further, a folder F1 corresponding to the Arc de Triomphe category that stores images classified in the Arc de Triomphe category, images classified in the Eiffel Tower category, and images classified in the Notre Dame category in the lower layer of the Paris folder. 1-1, folder F1-1-2 corresponding to the category of Eiffel Tower and folder F1-1-3 corresponding to the category of Notre Dame are created.

  Although a name is given to the folder for explanation here, the name is not actually given to the folder, and a number corresponding to the hierarchy is simply given as shown in FIG. Only. For this reason, the user does not know which image is classified in which folder until the folder is opened.

  The importance setting unit 32 sets the importance of each category using the classification result obtained by the image classification unit 30. Hereinafter, the setting of importance will be described.

  The importance setting unit 32 first groups similar images for each category to generate a similar image group. Here, as a grouping method, a correlation value is obtained for all the images for each category, and images having correlation values equal to or less than a predetermined value may be grouped as similar images. At this time, using one image as a reference, the correlation value is calculated by translating, enlarging, reducing, and rotating the other image, or changing the brightness and color in various ways. The degree of change may be determined in advance. However, since this method requires a long time for the grouping calculation, for example, the histogram and the color distribution of the image are obtained, and the correlation value is calculated between the images having the similar histogram and the color distribution to perform the grouping. It may be. Note that the method of grouping similar images is not limited to these methods.

  FIG. 5 is a diagram illustrating a grouping result between similar images. FIG. 5 shows a state where n similar image groups G1 to Gn have been created for images classified into a certain category (for example, the Arc de Triomphe folder). Note that images having no similar image are classified into a similar image group including only one image.

  Then, after grouping, the importance of the category is calculated based on the number of similar image groups and the number of images included in the similar image groups. Here, when shooting is performed using a digital camera or the like, an important scene cannot be recovered if shooting fails, so the number of shots increases, and as a result, similar images increase. In addition, there is a demand for capturing an image of a larger number of scenes and storing an image of the event as the event is more important.

  For this reason, the importance level setting unit 32 calculates the importance level using the following equation (1) according to the following three conditions.

Condition 1. The greater the number of images included in the similar image group, the higher the importance.

Condition 2. The importance is high when there are many similar image groups.

Condition 3. The more similar image groups with a larger number of images classified into similar image groups, the higher the importance.

Importance = a1 * M1 + b1 * M2 + c * M3 (1)
However, M1 is the total number of images included in all similar image groups (total number of similar images), M2 is the number of similar image groups, and M3 is a similar image group in which the number of classified images is a predetermined threshold value Th1 or more. The numbers a1, b1, and c1 are weighting factors, and in this embodiment, a1 = b1 = c1 = 1. Here, the total number of similar images is the number of images grouped into similar image groups other than the similar image group including only one image. Further, if there are three similar image groups A, B, and C, and three, four, and five images are included in each of them, assuming that the threshold Th1 is 4, for example, two similar image groups B , C meets condition 3, and 2 is added in equation (1). Here, the weighting factor is a1 = b1 = c1 = 1, but any of the total number of similar images, the number of similar image groups, and the number of similar image groups in which the number of classified images is equal to or greater than a predetermined threshold value Th1. Of course, the weighting coefficient may be changed depending on whether the emphasis is on.

  Thereby, the importance of each similar image group is calculated. For example, the importance is calculated such that similar image groups G1 = 100, G2 = 120..., Gn = 40. In calculating the importance, information on both the number of similar image groups and the number of images included in the similar image group is used. However, the number of similar image groups and the number of images included in the similar image group are used. Only one of the information may be used. In this case, only one of a1 × M1 and b1 × M2 in the above formula (1) is used for calculating the importance.

  When the CPU 12 starts up viewer software for browsing images stored in the hard disk 24, the importance setting unit 32 is displayed on the image list screen displayed on the display unit 18 by the viewer software. The set importance is displayed on the display unit 18 so as to be visible. FIG. 6 shows an example of an image list screen. As shown in FIG. 6, the image list screen 40 displays a folder field 40A indicating the folder structure stored in the hard disk 24 and a list of thumbnail images for images stored in the folder selected in the folder field 40A. A list display field 40B is included.

  In the folder field 40A, the hierarchical structure of folders corresponding to the hierarchical categories as described above is displayed. The display control unit 28 sets ranks 1 to 4 in categories according to the importance of each category set by the importance setting unit 32. Then, the folders are color-coded according to the order, and the hierarchical structure of the folders is displayed in the folder field 40A. For example, the first folder is pink, the second folder is orange, the third folder is yellow, and the fourth folder is green.

  For example, the order of importance according to the importance level is set to 1st place for importance levels over 200, 2nd place for importance levels over 150 and 200, 3rd place for importance levels over 100 and 150, The degree should be 4th.

  In addition to the method of color-coding according to importance, the order of importance may be displayed as a numerical value as shown in FIG. Also, a mark or graph corresponding to the importance may be displayed.

  Next, processing performed in the first embodiment will be described. FIG. 8 is a flowchart showing the processing performed in the first embodiment. It is assumed that image data representing an image is read from the memory card 2 and stored in a folder desired by the user of the hard disk 24. The viewer software is activated, and the processing is started when the user gives an instruction for classification from the input unit 16, and the image classification unit 30 stratifies the images and classifies them into a plurality of categories as described above (step ST1).

  Then, the importance setting unit 32 groups similar images (step ST2), and further sets the importance of the category (step ST3). Further, the display control unit 28 makes the importance level visible, displays the image list screen 40 on the display unit 18 (step ST4), and ends the process.

  As described above, in the first embodiment, since the importance of each category is set according to the number of similar image groups in each category and the number of images included in the similar image group, a burden is imposed on the user. Without being applied, it can be understood to which category the image is important.

  In addition, by displaying the importance levels set in a plurality of categories in a visually recognizable manner, the user can recognize the importance levels of each category at a glance by looking at the display result.

  Next, a second embodiment of the present invention will be described. In the first embodiment, the importance setting unit 32 sets the importance of each category. However, in the second embodiment, the importance setting unit 32 sets the importance of each category. Further, for images classified into similar image groups in each category, it is further important based on similar types of images included in similar image groups, the number of similar images according to similar types, and information on the degree of similarity. The degree is set. Hereinafter, the setting of the importance level for an image in the second embodiment will be described.

  First, the importance setting unit 32 classifies images included in the similar image group into smaller subgroups in consideration of parameters representing similar types. Specifically, among images included in the similar image group, a reference image serving as a reference for similarity determination is set, and a correlation value is calculated while moving all other images relative to the reference image. Other images having a value equal to or less than a predetermined value are classified into the same small group as the reference image. Since the images included in the small group are obtained by moving the camera in parallel and photographing the same scene, hereinafter, the small group is referred to as a parallel movement similar image, and the small group is referred to as a parallel movement similar image group. .

  Subsequently, with respect to the reference image included in the parallel movement similar image group, a correlation value is calculated while enlarging / reducing all the images other than the images included in the parallel movement similar image group, and the correlation value is a predetermined value. The following other images are classified into the same small group. Since the images included in the small group are obtained by changing the zoom magnification and photographing the same scene, the images are hereinafter referred to as an enlarged / reduced similar image, and the small group is referred to as an enlarged / reduced similar image group.

  Subsequently, all the images other than the images included in the parallel movement similar image group and the enlargement / reduction similar image group are rotated by 90 degrees with respect to the reference image included in the parallel movement similar image group, and then rotated by a predetermined angle. Then, the correlation value is calculated, and other images having the correlation value equal to or smaller than the predetermined value are classified into the same small group. Since the images included in the small group are obtained by rotating the camera 90 degrees and photographing the same scene, hereinafter, the small group is referred to as a rotation similar image group, and the small group is referred to as a rotation similar image group.

  Subsequently, the brightness of all the images other than the images included in the parallel movement similar image group, the enlargement / reduction similar image group, and the rotation similar image group is changed with respect to the reference image included in the parallel movement similar image group. Then, the correlation value is calculated, and other images having the correlation value equal to or smaller than the predetermined value are classified into the same small group. Since the images included in the small group are obtained by changing the exposure and photographing the same scene, the images are hereinafter referred to as brightness similar images, and the small groups are referred to as brightness similar image groups.

  Subsequently, with respect to the reference image included in the parallel movement similar image group, all other images other than the images included in the parallel movement similar image group, the enlargement / reduction similar image group, the rotation similar image group, and the brightness similar image group The correlation value is calculated while changing the color tone, and other images with the correlation value equal to or less than a predetermined value are classified into the same small group. Since the images included in the small group are obtained by changing the white balance and photographing the same scene, hereinafter, the images are referred to as color similar images, and this small group is referred to as a color similar image group. .

  FIGS. 9A to 9C are diagrams for explaining setting of importance in the second embodiment. As shown in FIG. 9A, in the second embodiment, images included in a certain similar image group (given as Gi) are translated similar image group G11, enlarged / reduced similar image group G12, and rotated similar image group. G13, brightness similar image group G14, and color similar image group G15. It should be noted that there are two images in the parallel movement similar image group G11, eight images in the enlargement / reduction similar image group G12, three images in the rotation similar image group G13, one image in the brightness similar image group G14, and a color similar image group G15. It is assumed that two images are classified respectively.

  Here, since important scenes cannot be recaptured if shooting fails, the number of times of shooting increases, so that similar images increase. Therefore, the importance setting unit 32 assigns evaluation points for setting importance to all the images included in the similar image group Gi in proportion to the number of images included in the similar image groups G11 to G15. To do. Note that a group including only one image has an evaluation score of 0. For example, 2 points for images classified into the parallel movement similar image group G11, 8 points for images classified into the enlargement / reduction similar image group G12, 3 points for images classified into the rotation similar image group G13, brightness 0 points are assigned to images classified into the similar image group G14, and 2 evaluation points are assigned to images classified into the color similar image group G15 (see FIG. 9B).

  Next, the importance setting unit 32 adds / subtracts evaluation points to / from the images classified into the similar image groups according to the types of parameters considered when the similar image group Gi is classified into the small groups. At this time, in each similar image group, different evaluation points are added or subtracted depending on the degree of similarity. For example, in the enlargement / reduction similar image group G12, the higher the enlargement ratio with respect to the reference image, the higher the score. In the brightness similar image group G14, the average value of the brightness including the reference image is calculated, and the negative evaluation score is added to the brighter and darker images than the average value, that is, the evaluation score is subtracted. Further, in the color similar image group G15, the average value of the color temperature including the reference image is calculated, and the negative evaluation score is added to the image having the higher and lower color temperature than the average value, that is, the evaluation score is subtracted. .

  For example, an image classified into the translation similar image group G11 has one point, an image classified into the enlargement / reduction similar image group G12 has two points, an image classified into the rotation similar image group G13 has three points, Evaluation points of -5 points are assigned to images classified into the brightness similar image group G14, and 0 points and -2 points are assigned to images classified into the color similar image group G15 (see FIG. 9C). ). Thereby, the importance is set for each image. The evaluation value corresponding to the parameter may be set in advance according to which parameter is important.

  The display control unit 28 displays an image list screen. FIG. 10 is a diagram showing an image list screen in the second embodiment. As shown in FIG. 10, the image list screen in the second embodiment includes a user field in addition to the folder field 42A and the list display field 42B corresponding to the folder field 40A and the list display field 40B of the image list screen 40 shown in FIG. A representative image display field 42 </ b> C for displaying a predetermined number of images with higher importance as representative images among the images stored in the selected folder is displayed. In FIG. 10, three representative images are displayed in the representative image display field 42C. The representative image display field 42 </ b> C is assigned “recommended” characters indicating that the representative image is an image recommended for printing to the user.

  As shown in FIG. 11, the display control unit 28 may display an evaluation score below each thumbnail image in the list of thumbnail images displayed in the list display field 42B. Thereby, the user can recognize the evaluation score of the image stored in the selected folder. Although the representative image display field 42C is not displayed in FIG. 11, it is needless to say that this may be displayed.

  Next, processing performed in the second embodiment will be described. FIG. 12 is a flowchart showing processing performed in the second embodiment. It is assumed that the image data is read from the memory card 2 and stored in a folder desired by the user of the hard disk 24. The viewer software is activated, and the processing is started when the user gives an instruction for classification from the input unit 16, and the image classification unit 30 stratifies the image data and classifies it into a plurality of categories as described above (step ST11).

  Then, the importance setting unit 32 groups similar images (step ST12), and sets the importance of the category (step ST13). Furthermore, the importance level setting unit 32 further classifies the similar image groups into small groups, and sets the importance levels of the images classified into the respective similar image groups as described above (step ST14). Then, the display control unit 28 makes the importance set for the category and the importance set for the image visible, displays the image list screen 42 on the display unit 18 (step ST15), and ends the process. In the second embodiment, to display the importance set in the image so as to be visible, the representative image is displayed in the representative image display field 42C as shown in FIG. 10 and the list display field 42B is displayed. This means that the importance is displayed below the displayed thumbnail image.

  As described above, in the second embodiment, similar images included in similar image groups are included in each similar image group based on information on the similar types, the number of similar images corresponding to the similar types, and the degree of similarity. Since the importance of the image is further set, not only which category is important but also which image included in the category is important.

  In the second embodiment, the images included in each similar image group based on the similar types in the images included in the similar image groups, the number of similar images according to the similar types, and the information on the degree of similarity. Is set to each similar image group by using at least one of the similar type in the images included in the similar image group, the number of similar images according to the similar type, and the similarity degree information. You may set the importance of the image contained.

  In the second embodiment, the degree of importance may be set more finely using information such as the degree of blur of the image, the facial expression of the person included in the image, and the type of person included. Good.

  In the second embodiment, the upper predetermined number of images are selected as representative images in the images included in the folder selected by the user. However, before the user selects the folder, the most important level is the highest. A representative image may be selected from the category and displayed in the representative image display field 42C. Alternatively, one representative image may be selected from the top several categories (for example, three) when the categories are sorted according to importance, and displayed in the representative image display field 42C.

  In the first and second embodiments, the importance level is set for each category. For example, the importance level is already set for each category by using another method or manually by the user. There may be. In such a case, it is only necessary to newly set the importance of each category by adding the importance set according to the present embodiment to the importance set in advance.

  In the first and second embodiments, images are hierarchized and classified into a plurality of categories based on the interval between shooting dates and times. For example, GPS information indicating the latitude and longitude of a shooting location can be acquired. When shooting is performed using a simple camera, GPS information is added to the image. In such a case, images may be hierarchized and classified for each shooting location using GPS information.

  In addition, a method of classifying images into categories according to the user's schedule with reference to schedule information representing the user's schedule as described in Patent Document 2, and a plurality of methods as described in Patent Document 3 The feature amount may be calculated from the image, and the image may be classified using a method of classifying the image into a plurality of categories based on the relationship between the feature amounts of the image.

  Furthermore, it goes without saying that the user may classify the images according to his / her preference by manual operation.

  As described above, the apparatuses according to the first and second embodiments of the present invention have been described. However, the computer functions as a unit corresponding to the importance setting unit 32, and the processes shown in FIGS. 8 and 12 are performed. The program to be executed is also one embodiment of the present invention. A computer-readable recording medium in which such a program is recorded is also one embodiment of the present invention.

1 is a schematic block diagram showing the configuration of an importance setting device according to a first embodiment of the present invention. The figure which shows the time table of one day with the user's trip to France A diagram showing the relationship between the time axis of travel in France and the number of images Figure showing the folder structure after classification The figure which shows the grouping result of similar images The figure which shows the image list screen in 1st Embodiment (the 1) The figure which shows the image list screen in 1st Embodiment (the 2) The flowchart which shows the process performed in 1st Embodiment. The figure for demonstrating the setting of the importance in 2nd Embodiment The figure which shows the image list screen in 2nd Embodiment (the 1) The figure which shows the image list screen in 2nd Embodiment (the 2) The flowchart which shows the process performed in 2nd Embodiment.

Explanation of symbols

1 Importance setting device 2 Memory card 12 CPU
Reference Signs List 14 system memory 16 input unit 18 display unit 20 card slot 22 compression / decompression unit 24 hard disk 26 memory control unit 28 display control unit 30 image classification unit 32 importance setting unit 40, 42 image list screen

Claims (7)

Grouping means for grouping similar images and generating a similar image group for each category obtained by classifying a plurality of images into a plurality of categories;
Importance setting, comprising: setting means for setting importance of each category according to the number of similar image groups in each category and / or the number of images included in the similar image groups apparatus.   The setting means includes an image included in the similar image group based on at least one information of a similar type in the image included in the similar image group, the number of similar images according to the similar type, and a similarity degree. 2. The importance setting apparatus according to claim 1, wherein the importance setting device further sets the importance. Display means for performing various displays;
The importance setting apparatus according to claim 1, further comprising display control means for displaying the importance of each category on the display means so as to be visible. Display means for performing various displays;
The importance setting apparatus according to claim 2, further comprising display control means for displaying the importance of each category and the importance of the image on the display means so as to be visible. The setting unit is a unit that selects a predetermined number of images with the highest importance as representative images of the plurality of images,
5. The importance setting apparatus according to claim 4, wherein the display control means is means for displaying the representative image on the display means. For each category obtained by classifying multiple images into multiple categories, generate similar image groups by grouping similar images together,
An importance setting method, wherein the importance of each category is set according to the number of the similar image groups in each category and / or the number of images included in the similar image group. For each category obtained by classifying a plurality of images into a plurality of categories, a procedure for grouping similar images and generating a similar image group,
And a procedure for setting importance of each category according to the number of the similar image groups in each category and / or the number of images included in the similar image group. A program that causes a computer to execute.

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