JP2004228995A - Image trimming device, image trimming method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image trimming device which calculates a noticed area and an object index of an image to automatically and effectively trim the image. <P>SOLUTION: The image trimming device comprises an image read means for reading an image as a trimming object; a noticed area calculation means for calculating a noticed area of the image, which is a trimming object; an object storage means for storing object models; an object index calculation means for calculating indexes of objects in the image on the basis of the object models; a trimming method storage means for storing trimming methods; a trimming method determination means for determining a trimming method on the basis of the degree of notice calculated by the noticed area calculation means and object index values calculated by the object index calculation means; and a trimming means for trimming the image in accordance with the determined trimming method. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an image trimming apparatus, an image trimming method, and a program for automatically trimming (cutting or cutting out part of an image) a digital image (also simply referred to as an “image”) captured by a digital camera or the like. About.
[0002]
[Prior art]
2. Description of the Related Art In recent years, digital cameras have become widespread, and it has become possible to easily capture digital images captured in a personal computer (PC), edit the digital images, and print the digital images. For example, it has become possible to edit a digital image taken by a digital camera at a travel destination and easily create a travel album or the like.
When editing these digital images, as shown in FIG. 6, FIG. 7, or FIG. 8, it is often desired to edit a part of the image (a region of interest in the image) by trimming.
[0003]
Conventionally, such trimming has been performed manually by manipulating a mouse or the like so as to cut (cut out) a region of interest (conspicuous region) in a digital image.
This manual trimming does not take much trouble when the number of digital images is small, but when trimming a large number of digital images and editing them as an album, manual trimming work is quite sufficient. This is an annoying task.
In addition, with the spread of digital cameras, the range of users has expanded, and some users are not accustomed to PC operation. Therefore, it has been desired to provide a device that can easily perform digital image trimming.
[0004]
As a prior art regarding such digital image trimming, for example, there is Patent Document 1 “Image Combining Apparatus”.
The invention disclosed in Patent Document 1 is an image synthesizing apparatus for easily creating a synthesized image in which a main image is superimposed on a background image. In the overlay composition in this image composition device, an outer frame and an inner frame located inside the outer frame are displayed in the sub-display area. Then, the captured background image is displayed in the main display area. In the main display area, a trimming frame corresponding to the outer frame of the sub display area is displayed, and the trimming frame is moved to specify a range to be synthesized. When the paste operation is performed, the background image is pasted in the outer frame in the sub display area. Next, another image (main image) is fetched and displayed in the main display area. In this main display area, a trimming frame corresponding to the inner frame of the sub display area is displayed, a portion specified by this trimming frame is fitted into the inner frame of the sub display area, and the main image is superimposed on the background image. The obtained composite image is completed.
[0005]
[Patent Document 1]
Japanese Patent Application Laid-Open No. 2002-10056
[Problems to be solved by the invention]
However, the invention disclosed in Patent Document 1 is an invention in which a user himself manually cuts out (crops) a trimming range of each image to be trimmed, and synthesizes the images. That is, in the present invention, it is necessary for the user to perform the work of cutting out (cutting out) the attention area while visually checking the attention area (conspicuous area) in each image to be trimmed. For this reason, when editing a large number of images by trimming, the work load becomes large, and a large amount of work time is required, which is a cumbersome work.
[0007]
The present invention has been made in order to solve such a problem, and an object thereof is to automatically trim a region of interest in an image, so that the image can be trimmed more easily and effectively. An object of the present invention is to provide an image trimming device, an image trimming method, and a program.
[0008]
[Means for Solving the Problems]
SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and an image trimming apparatus of the present invention is an image trimming apparatus for trimming an image, comprising: an image reading unit that reads an image to be trimmed; A region of interest calculation unit that calculates a region of interest of an image to be processed, an object storage unit that stores a model of an object learned in advance for a predetermined object, and a model of the object stored in the object storage unit. Object index calculating means for calculating an index of an object appearing in an image; a trimming technique storing means for storing a trimming technique; an attention level obtained by the attention area calculating means; The trimming method according to the object index And a trimming method determining means, in accordance with the trimming method that has been determined by the trimming method determining means, characterized by comprising a trimming unit for trimming the image.
With such a configuration, the image to be trimmed is read by the image reading means, and the attention area of the image to be trimmed is calculated by the attention area calculation means. Further, a model of the object from which the object (such as a human face) has been learned is stored in advance in an object storage means (such as a database). Further, the object index calculating means calculates the index of the object appearing in the image based on the object model stored in the object storage means. Then, the trimming method (trimming shape or the like) is stored in advance by the trimming method storage means, and the trimming method is determined by the trimming method determining means according to the degree of interest obtained by the attention area calculating means and the object index. Then, the image is trimmed by the trimming means according to the trimming technique determined by the trimming technique determining means.
As a result, the image can be trimmed using both the attention level and the object index, and the important region in the image can be trimmed without being hidden.
[0009]
Further, the image trimming apparatus of the present invention is characterized in that, when trimming is performed by the trimming means, trimming is performed so as to always include a place where the value of the object index exceeds a predetermined threshold.
With such a configuration, the image is trimmed so as to include all the regions where the value of the object index calculated by the object index calculating means exceeds a predetermined threshold.
As a result, it is possible to prevent a place where the object is reflected (for example, a face image) from being cut.
[0010]
Further, in the image trimming apparatus according to the present invention, the trimming method storage means is means for storing a trimming template based on an object index and a method of applying the template.
With such a configuration, various templates (templates describing a trimming shape, an application method, and the like) selectively used in accordance with the value of the object index are stored in the trimming method storage unit.
As a result, the image can be automatically trimmed by selecting the optimum shape from various shapes.
[0011]
Further, the image trimming device of the present invention is characterized in that the trimming method determining means determines a template to be used for trimming from the template based on an object index.
With such a configuration, the optimum template to be used for trimming is determined from various templates (templates describing a trimming shape, an application method, and the like) according to the value of the object index by the trimming method determining unit. .
As a result, the image can be automatically trimmed by selecting the optimum shape from various shapes.
[0012]
Further, the image trimming device of the present invention is characterized in that the index calculated by the object index calculating means is an index for determining whether or not a face image is included.
With such a configuration, the object index calculating means calculates an object index indicating whether or not the image is a face image by referring to an object model previously learned about a human face.
Thus, a portion that includes a face and has a high attention area can be effectively trimmed.
[0013]
Further, the image trimming method of the present invention is an image trimming method in an image trimming device for trimming an image, wherein an image reading procedure for reading an image to be trimmed, and an attention area of the image to be trimmed are performed. An attention area calculation procedure to be calculated; an object storage procedure to store a model of an object learned in advance for a predetermined object; and an index of an object appearing in an image based on the object model stored in the object storage procedure. , A trimming method storing procedure for storing a trimming technique, a degree of attention obtained by the attention area calculating procedure, and a trimming method are determined in accordance with the object index obtained by the object index calculating procedure. Trimming method decision And procedures, in accordance with the trimming method that is determined by the trimming method decision procedure, characterized in that it comprises a trimming procedure for trimming the image.
In such a procedure, the image to be trimmed is read by the image reading procedure, and the attention area of the image to be trimmed is calculated by the attention area calculation procedure. In addition, a model of an object that has learned an object (such as a human face) is stored in advance in an object storage procedure (such as a database). In addition, in accordance with the object index calculation procedure, the index of the object appearing in the image is calculated based on the object model stored in the object storage procedure. Then, the trimming method (trimming shape or the like) is stored in advance by the trimming method storage procedure, and the trimming method is determined according to the degree of interest obtained by the attention area calculation procedure and the object index by the trimming method determination procedure. Then, according to the trimming procedure, the image is trimmed according to the trimming technique determined by the trimming technique determination procedure.
As a result, the image can be trimmed using both the attention level and the object index, and the important region in the image can be trimmed without being hidden.
[0014]
Further, the computer program of the present invention includes a computer in an image trimming apparatus for trimming an image, an image reading procedure for reading an image to be trimmed, and an attention area calculation for calculating an attention area of the image to be trimmed. A procedure, an object storage procedure for storing a model of an object learned in advance for a predetermined object, and an object index for calculating an index of an object appearing in an image based on the object model stored in the object storage procedure. A calculating procedure, a trimming technique storing procedure for storing a trimming technique, a degree of attention obtained by the attention area calculating procedure, and a trimming technique determining procedure for determining a trimming method according to the object index obtained by the object index calculating procedure. And said According Liming method determines trimming method determined by the procedure, a program for executing a trimming procedure for trimming the image.
[0015]
BEST MODE FOR CARRYING OUT THE INVENTION
Next, embodiments of the present invention will be described with reference to the drawings.
[0016]
FIG. 1 is a block diagram illustrating a configuration example of a unit included in the image trimming device 100 according to the present invention. Each means provided in the image trimming device 100 shown in FIG. 1 has the following functions.
(1) Image reading means 101
The image reading unit 101 is a unit for reading a value (RGB value or the like) for each pixel constituting an image.
[0017]
(2) Attention area calculation means 102
The attention area calculation means 102 is a means for calculating the attention area of the image to be trimmed, and divides the image into a plurality of areas, and sets the degree of attention to two or more levels for each of the separated areas. Classify. For the calculation of the attention area, for example, the “attraction degree” disclosed in “Japanese Patent Application Laid-Open No. 2001-126070 (attention area extraction apparatus and automatic composition determination apparatus using the attention area extraction apparatus)” can be used. Regarding the degree, an outline will be described in the item of “Supplementary explanation on the degree of attraction” described later.)
[0018]
FIG. 2 is a diagram schematically showing a first image 11 to be subjected to a trimming process, and FIG. 3 is a diagram showing the degree of attention (attraction degree) of the image 11 shown in FIG. FIG. 4 is a diagram schematically illustrating an image 12, in which an attention area 12a of a flower part and an attention area 12b of a person part are calculated to have a high degree of attention (attraction degree).
[0019]
(3) Object storage means 103
The object storage unit 103 is a unit for storing a model of an object learned in advance. In the present embodiment, it is assumed that whether or not the object model is a face image has been learned. The object model is, for example, a model learned by a neural network. In addition, a stochastic model represented by Naive Bayes may be used, and MH. Yang, D.M. Roth and N.M. A model learned by the method disclosed in Ahuja “A SNOW based Face Detector, NIPS-12, Dec, 1999” may be used.
For example, in the SNoW learning method, an object is learned for a face image as follows.
(1) Prepare a database of face images.
{Circle around (2)} Prepare a database (eg, car, food) that is not a face image.
{Circle around (3)} The object model is repeatedly learned by the SNoW learning method described in the above-mentioned paper, with the face image data as the teacher signal “1” and the data other than the face image as the teacher signal “0”.
[0020]
(4) Object index calculating means 104
The object index calculation unit 104 performs a scan process for extracting the pixels obtained from the image reading unit 101 for the input pixels of the object model and obtaining an object index. Thereafter, the pixels shifted slightly are read, the object index value is calculated in the same manner, and the processing is performed on the entire image. As a result, the part with the highest object index is passed to the next processing as the processing result of the object index calculating means 104.
[0021]
FIG. 4 is a diagram illustrating an example of an image 13 indicating a region having the highest object index value based on the calculation result of the object index value of the image illustrated in FIG. In the present embodiment, as a result of applying the object index value calculation processing to the image of FIG. 2, the area 13a shown in FIG. 4 is extracted as the highest area. It is assumed that the object index value at this time is calculated as “object index value = g (image)”.
Further, in the example of the present embodiment, it is assumed that the object storage unit 103 stores an object model that has been learned whether or not the image is a face image. It is assumed that an index of whether or not there is is calculated.
The object index value indicates the likelihood that the image is a face image, and “1” is an index indicating an area most likely to be a face image, and “0” is an index indicating an area that is not a face image.
Note that, in the example of the present embodiment, the region having the highest object index is determined as the object position. However, the present invention is not limited to this. Good. Thereby, for example, when a plurality of face images are included, all the positions of the face images can be specified.
[0022]
(5) Trimming method storage means 105
This is a means for storing a method of performing trimming, and stores a trimming shape (template), an object index value, and an image conversion processing method.
[0023]
FIG. 5 is a diagram illustrating an example of data stored in the trimming method storage unit 105. As shown in FIG. 5, the trimming method storage unit 105 stores a trimming shape (template), an object index value, and an image conversion processing method (whether enlargement / reduction and rotation are possible).
The pattern 21 is trimmed into a circle, is applied when the face index is lower than 0.1, and indicates a condition that enlargement / reduction is possible from 90% to 120% and rotation is not permitted.
The pattern 22 is trimmed into a heart shape, is applied when the face index is higher than 0.9, and indicates a condition that scaling can be performed from 90% to 120% and rotation is not permitted.
Pattern 23 indicates a condition that the pattern is trimmed into a star shape, the face index is applied between 0.7 and 0.8, no scaling is performed, and rotation is not permitted.
[0024]
(6) Trimming method determining means 106
The trimming method determining unit 106 stores a trimming method (a template indicating a trimming shape or the like) from the trimming method storage unit 105 based on the object index (the value with the highest object index in the entire image) obtained by the object index calculating unit 104. ).
For example, if the object index obtained by the object index calculation unit 104 is “g (image) = 0.92”, the trimming method determination unit 106 determines the pattern 22 (FIG. 5) whose face index is larger than 0.9. select.
Similarly, when “g (image) = 0.75”, the pattern 23 is selected.
Further, it is assumed that, for example, an object model of a car image is stored in the object storage unit 103, and “g (image) = 0.08”. In this case, the pattern 21 is selected.
[0025]
(7) Trimming means 107
The trimming unit 107 performs a trimming process according to the trimming method (pattern) determined by the trimming method determining unit 106. When performing the trimming process, the object region determined by the object index calculation unit 104 is always included, and the trimming process is performed so as to include the attention region as much as possible.
[0026]
FIG. 6 is a diagram showing an example in which the pattern 22 shown in FIG. 5 is applied to the image shown in FIG. FIG. 7 is a diagram showing an example in which the pattern 23 shown in FIG. 5 is applied to the image shown in FIG. FIG. 8 is a diagram showing an example in which the pattern 21 shown in FIG. 5 is applied to the image shown in FIG.
In the example shown in FIG. 6, the trimming processing is performed so that the trimming shape of the pattern 22 (FIG. 5) always includes the object area and includes the attention area as much as possible. As a result, the pattern 22 is expanded to the maximum (120% of the upper limit), and a trimming result as shown in FIG. 6 is obtained.
Similarly, when “g (image) = 0.75”, the pattern 23 (FIG. 5) is selected, and the trimming result as shown in FIG. 7 is obtained.
It is assumed that the object model of the car image is stored in the object storage unit 103, and if g (image) = 0.08 ", the pattern 21 is selected and the trimming result shown in FIG. 8 is obtained.
[0027]
The following means are included in the trimming means 107, and the above-described trimming processing is performed using the following means. That is, the trimming is performed so that the attention area is included as much as possible in the trimming shape (template) together with the face image.
The attention area error calculation means 107a is means for calculating the degree (area) of the attention area included in the pattern. When the attention area error calculating means 107a is used, the position of the template is determined so that the area of the attention area included in the template is as large as possible.
The image scaling means 107b is a means for changing the trimming shape, the attention area, or the size of the original image based on the calculation result of the attention area error calculation means 107a.
The image rotation unit 107c is a unit for rotating the trimming shape, the region of interest, or the original image based on the calculation result of the region of interest error calculation unit 107a.
[0028]
In this manner, the image trimming device of the present invention can calculate the attention area and the object index for the image to be subjected to the trimming process, and perform the automatic trimming. In addition, automatic trimming including a region of interest using human visual characteristics can be performed.
[0029]
FIG. 9 is a diagram showing a processing procedure in the image trimming apparatus of the present invention, and is a flowchart showing a procedure for calculating an object index and performing a trimming process. Hereinafter, the procedure of trimming performed by the image trimming apparatus of the present invention will be described with reference to the flowchart shown in FIG.
(1) Step S1: A value for each pixel of an image to be processed is read.
(2) Step S2: The attention area calculation means 102 calculates an attention degree (attraction degree).
For example, the degree of attention (attraction degree) of the image 11 shown in FIG. 2 is calculated, and the pattern image 12 of the attention area shown in FIG. 3 is obtained.
(3) Step S3: The object index value and the object area are calculated.
(4) Step S4: A trimming rule is determined according to the object index value.
(5) Step S5: Trimming is performed.
[0030]
FIG. 10 is a block diagram showing a configuration example of the image trimming apparatus of the present invention, and shows only a portion directly related to the present invention. 10, reference numeral 100 denotes an image trimming device, 1 denotes a communication network such as the Internet network, 111 denotes a communication interface for connecting the image trimming device to the communication network 1, 112 denotes a control unit that controls the entire image trimming device, 120 Indicates a processing program unit. Note that the image trimming apparatus according to the present invention can be realized even by using a PC (personal computer).
[0031]
Further, the processing program unit 120 includes the following processing units.
The image selection processing unit 121 is a processing unit for selecting an image to be trimmed from the image database 113.
The image reading processing unit 122 is a processing unit for reading a value (RGB value or the like) for each pixel constituting an image to be trimmed.
The attention area calculation processing section 123 is a processing section for calculating an attention area of an image to be trimmed. The image is divided into a plurality of regions, and the degree of attention is divided into two or more levels for each of the separated regions. For the calculation of the attention area, for example, the “attraction degree” disclosed in “Japanese Patent Application Laid-Open No. 2001-126070 (attention area extraction apparatus and automatic composition determination apparatus using the same)” can be used.
[0032]
The object index calculation processing unit 124 is a processing unit for calculating an index of an object appearing in an image based on object model data stored in the object database 114.
The trimming method determination processing unit 125 records in the trimming method database 115 based on the degree of attention (attraction degree) obtained by the attention area calculation processing unit 123 and the object index obtained by the object index calculation processing unit 124. A processing unit for determining a trimming method with reference to the template data obtained.
[0033]
The trimming processing unit 126 is a processing unit for trimming an image according to the trimming method determined by the trimming method determination processing unit 125. When executing the trimming processing, the functions of the attention area error calculation processing unit 126a, the image scaling processing unit 126b, and the image rotation processing unit 126c are used.
The attention area error calculation processing unit 126a is a processing unit for calculating the degree to which the attention area is hidden or cut by the template used for trimming (the degree to be included in the template). The template is template data stored in the trimming method database 115.
[0034]
The image scaling processing unit 126b is a processing unit for changing the trimming shape (template), the attention area, or the size of the original image based on the calculation result of the attention area error calculation processing unit 126a.
The image rotation processing unit 126c is a processing unit for rotating the trimming shape (template), the attention area, or the original image based on the calculation result of the attention area error calculation processing unit 126a.
The display processing unit 127 is a processing unit for displaying the result of the trimming on the display device.
The print processing unit 128 is a processing unit for printing a result of trimming.
The image database 113 stores image data to be trimmed and the trimmed image data, and the object database 114 stores data of an object model. The trimming method database 115 stores template data describing a method of performing trimming, and stores a trimming shape, an object index value, an image conversion processing method, and the like.
[0035]
Note that the processing program unit 120 may be realized by dedicated hardware, and the processing program unit includes a general-purpose information processing device such as a memory and a CPU (central processing unit). A program (not shown) for realizing the function of the unit may be loaded into a memory and executed to realize the function. Further, it is assumed that an input device, a display device, a printing device, and the like (all are not shown) are connected to the image trimming device 100 as peripheral devices. Here, the input device refers to an input device such as a keyboard and a mouse. The display device refers to a CRT (Cathode Ray Tube), a liquid crystal display device, or the like. The printing device refers to a laser printer, an inkjet printer, or the like.
[0036]
Further, a program for realizing the functions of the image trimming apparatus 100 shown in FIG. 10 is recorded on a computer-readable recording medium, and the program recorded on this recording medium is read by a computer system and executed to execute the present invention. Processing necessary for the image trimming apparatus 100 of the present invention may be performed. Here, the “computer system” includes an OS and hardware such as peripheral devices.
The “computer system” includes a homepage providing environment (or a display environment) if a WWW system is used.
The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and a storage device such as a hard disk built in a computer system.
Further, the “computer-readable recording medium” refers to a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, which dynamically holds the program for a short time. In this case, a medium holding a program for a certain period of time, such as a volatile memory in a computer system serving as a server or a client in that case, is also included.
Further, the above-mentioned program may be for realizing a part of the above-mentioned functions, and may be a program for realizing the above-mentioned functions in combination with a program already recorded in a computer system, that is, a so-called difference file (difference file). Program).
[0037]
As described above, the embodiments of the present invention have been described. However, the image trimming apparatus of the present invention is not limited to the illustrated example described above, and various changes can be made without departing from the gist of the present invention. Of course.
[0038]
[Supplementary explanation on the degree of attraction]
In the embodiment of the present invention, “attraction level” is used as a reference for extracting a region of interest. One example of a method of calculating the “attraction level” is disclosed in Japanese Patent Laid-Open No. 2001-126070 (attention region extraction device). And an automatic composition determination device using the same).
Here, only the outline will be described.
To extract a region of interest, the degree of attraction is evaluated according to the physical characteristics of the original image. Here, the degree of attraction means a parameter suitable for human subjectivity. The region of interest is extracted by extracting a region that stands out from the evaluation result as a region of interest. In other words, when the attention area is evaluated, the evaluation is performed in accordance with the human subjectivity according to the physical characteristics, so that the attention area suitable for the human subjectivity can be extracted.
For example, when the physical characteristics include the degree of heterogeneity of color, the degree of attraction can be evaluated based on the difference in color of each region.
In addition, since the physical characteristics further include, in addition to the color heterogeneity, the shape heterogeneity, the area heterogeneity, and the texture (pattern) heterogeneity, the physical heterogeneity is determined based on at least one of the four heterogeneities. By evaluating the degree of eye-catching, the degree of eye-catching can be accurately evaluated according to the characteristics of the original image.
In the case where three color components (hue, saturation, and lightness) are also evaluated, a region close to a prominent color (red) according to human subjectivity can be evaluated as the most prominent region.
Furthermore, by evaluating the spatial frequency and the area of each region in the original image, the evaluation of the most prominent region can be more accurately determined.
[0039]
The evaluation of the degree of attraction is performed according to the following procedure.
(1) First, the original image is divided into regions. In this case, the original image is divided into a drawing area and a picture area. The method of this area division is described in W.I. Y. Ma and B. S. A boundary detection method based on "edge flow" described in "Edge Flow: A Framework of Boundary Detection and Image Segmentation" by Manjunath et al. Is applied.
(2) Next, the divided figure region is extracted, and the degree of attraction of the region is evaluated.
The evaluation of the degree of attraction is roughly performed as follows.
First, determine the degree of heterogeneity attraction in each area. In this case, the heterogeneity of the color, the heterogeneity of the texture, the heterogeneity of the shape, and the heterogeneity of the area are respectively obtained, and weighting factors are assigned to each of them to perform linear combination, thereby obtaining the heterogeneous attractiveness of each region.
Next, the feature attractiveness in each area is obtained. In this case, the attraction of color, the attraction of spatial frequency, and the attraction of area are obtained, and weighting factors are assigned to each of them to perform linear combination to obtain the characteristic attraction of each region.
Next, the heterogeneous attractiveness and the feature attractiveness of each area are added to obtain a feature integrated value, and the feature integrated value is evaluated by a predetermined beta function to calculate the attractiveness.
(3) Also, a pattern diagram in which the degree of attraction is evaluated is generated from the original image.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration example of a unit provided in an image trimming device of the present invention.
FIG. 2 is a diagram schematically showing an image to be trimmed.
FIG. 3 is a diagram schematically showing a region of interest (attraction level) of the image shown in FIG. 2;
FIG. 4 is a diagram showing a region having the highest object index value in the image of FIG. 2;
FIG. 5 is a diagram showing an example of data stored in a trimming method storage unit.
FIG. 6 is a diagram showing an example in which a pattern 22 shown in FIG. 5 is applied to the image of FIG. 2;
FIG. 7 is a view showing an example in which a pattern 23 shown in FIG. 5 is applied to the image of FIG. 2;
FIG. 8 is a view showing an example in which the pattern 21 shown in FIG. 5 is applied to the image of FIG. 2;
FIG. 9 is a diagram showing a processing procedure in the image trimming device of the present invention.
FIG. 10 is a block diagram illustrating a configuration example of an image trimming device according to the present invention.
[Explanation of symbols]
REFERENCE SIGNS LIST 100 image trimming device, 101 image reading means 102 attention area calculation means, 103 object storage means 104 object index calculation means, 105 trimming technique storage means 106 trimming technique determination means, 107 trimming means 107a attention area error calculation means, 107b image scaling means 107c Image rotation means

Claims (7)

An image trimming device for trimming an image,
Image reading means for reading an image to be trimmed;
An attention area calculation unit that calculates an attention area of an image to be trimmed;
Object storage means for storing a model of an object learned in advance for a predetermined object;
Based on the model of the object stored in the object storage means, an object index calculation means for calculating the index of the object shown in the image,
Trimming method storage means for storing a trimming method;
Trimming method determining means for determining a trimming method according to the degree of attention obtained by the attention area calculating means and the object index obtained by the object index calculating means;
An image trimming apparatus comprising: a trimming unit that trims an image according to a trimming method determined by the trimming method determining unit. 2. The image trimming apparatus according to claim 1, wherein when trimming is performed by the trimming unit, trimming is performed so as to always include a place where the value of the object index exceeds a predetermined threshold. 3. The image trimming apparatus according to claim 1, wherein the trimming method storage unit is a unit that stores a trimming template based on an object index and a method of applying the template. 4. The image trimming apparatus according to claim 3, wherein said trimming method determining means determines a template to be used for trimming from the template based on an object index. The image trimming device according to claim 1, wherein the index calculated by the object index calculation unit is an index for determining whether or not a face image is included. An image trimming method in an image trimming device for trimming an image,
An image reading procedure for reading an image to be trimmed;
An attention area calculation procedure for calculating an attention area of an image to be trimmed;
An object storage procedure for storing an object model learned in advance for a predetermined object;
Based on the model of the object stored in the object storage procedure, an object index calculation procedure for calculating the index of the object shown in the image,
A trimming method storing procedure for storing the trimming method;
A trimming method determining step of determining a trimming method according to the degree of attention obtained by the attention area calculation procedure and the object index obtained by the object index calculation procedure;
A trimming procedure for trimming an image according to a trimming technique determined by the trimming technique determination procedure. The computer in the image trimming device that trims the image
An image reading procedure for reading an image to be trimmed;
An attention area calculation procedure for calculating an attention area of an image to be trimmed;
An object storage procedure for storing an object model learned in advance for a predetermined object;
Based on the model of the object stored in the object storage procedure, an object index calculation procedure for calculating the index of the object shown in the image,
A trimming method storing procedure for storing the trimming method;
A trimming method determining step of determining a trimming method according to the degree of attention obtained by the attention area calculation procedure and the object index obtained by the object index calculation procedure;
A program for executing a trimming procedure for trimming an image in accordance with a trimming technique determined by the trimming technique determination procedure.

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