Information management apparatus, information management method, and computer readable medium which determine similarities

An information management apparatus includes a characteristic-amount management unit managing a characteristic amount; a characteristic-amount association unit maintaining an association with a similar characteristic amount for each of the characteristic amounts; a space index management unit managing a space index for the characteristic amounts managed by the characteristic-amount management unit; a partial space determination unit determining a partial space in the space index to which a first characteristic amount belongs in accordance with a request for retrieving a characteristic amount similar to the first characteristic amount; and a similarity determination unit calculating similarity between the first characteristic amount and a second characteristic amount and between the first characteristic amount and a third characteristic amount associated with the second characteristic amount by the characteristic-amount association unit, and determines which characteristic amounts are similar to the first characteristic amount by comparing the calculated similarity and a predetermined threshold.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an information management apparatus, an information management method, and a program.

2. Description of the Related Art

In recent years and continuing to the present, techniques of retrieving an image similar to a specific image from an image database using the specific image as a retrieval condition have been proposed and implemented as products.

Generally, in the similar-image retrieval techniques, a characteristic value (hereinafter referred to as a “characteristic amount”), which is obtained by the analysis of an image and serves as information for indicating the characteristic of the image, is registered at the same time as when the image is registered. In retrieving a similar image, the similarity between the characteristic amounts is calculated. If the similarity is greater than a predetermined threshold, an image corresponding to the characteristic amount is retrieved as the similar image.

As a method for extracting a characteristic amount, Patent Document 1, for example, proposes a characteristic-amount extraction technique that enables the automatic extraction of the characteristic amount corresponding to a human sense so that a human can retrieve a similar image adapted to his or her sense of similarity.

In such a similar-image retrieval technique using the characteristic amount, the characteristic amount itself is extracted in advance and stored at the time of registering an image, and a similar image can be retrieved only by the characteristic amount without reference to the image. Therefore, the retrieval of similar-image information can be executed at relatively high speed.

However, the similar-image retrieval technique described above is required to calculate the similarity between the characteristic amount and characteristic amounts of all registration images point by point at the time of retrieving the similar image. Unfortunately, processing speed for retrieving the similar image decreases as the number of registration images is increased.

In order to deal with this problem, Patent Document 2 proposes a technique in which a list of images similar to an image is retrieved in advance from an image database at the time of registering the image and is stored, and the list is used at the time of retrieving a similar image. This technique does not require the processing for calculating the similarity at the time of retrieving the similar image, which in turn can accelerate the processing for retrieving the similar image even where the number of registration images is increased.

The technique described in Patent Document 2 makes it possible to retrieve an image similar to a previously accumulated image at high speed, but it is not targeted for retrieving an image similar to a non-accumulated image.

SUMMARY OF THE INVENTION

The present invention has been made in view of the above problems and may provide an information management apparatus, an information management method, and a program capable of appropriately accelerating the retrieval of similar information.

In order to solve the above problems, there is provided an information management apparatus including a characteristic-amount management unit that manages a characteristic amount for each piece of information; a characteristic-amount association unit that maintains an association with a similar characteristic amount among characteristic amounts managed by the characteristic-amount management unit for each of the characteristic amounts; a space index management unit that manages a space index for the characteristic amounts managed by the characteristic-amount management unit; a partial space determination unit that determines a partial space in the space index to which a first characteristic amount belongs in accordance with a request for retrieving a characteristic amount similar to the first characteristic amount; and a similarity determination unit that calculates similarity between the first characteristic amount and a second characteristic amount other than the first characteristic amount belonging to the partial space and between the first characteristic amount and a third characteristic amount associated with the second characteristic amount by the characteristic-amount association unit, and determines which characteristic amounts are similar to the first characteristic amount based on a comparison between the calculated similarity and a predetermined threshold.

With this information management apparatus, it is possible to appropriately accelerate the retrieval of similar information.

According to an embodiment of the present invention, it is possible to provide an information management apparatus, an information management method, and a program capable of appropriately accelerating the retrieval of similar information.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the accompanying drawings, an embodiment of the present invention is described below. As an example for describing the embodiment, the present invention is applied to similar-image retrieval processing in which image information is registered as information and an image similar to the image information is retrieved. Note that information to be handled is not limited to image information, but it may be of any information so long as its characteristic amount is used to make a comparison to retrieve similar information. For example, the present invention may be applied to the management of document data, voice data, moving-image data, analysis data of information output after being analyzed, etc.

FIG. 1is a block diagram showing a function configuration example of an information management apparatus according to the embodiment of the present invention. InFIG. 1, an information management apparatus100is connected to plural client PCs (Personal Computers)200aand200b(hereinafter simply referred to as a “client PC”), each having a user I/F201for inputting retrieval conditions and displaying retrieval results, via networks (regardless of whether they are wired or wireless) such as the Internet or a LAN (Local Area Network).

The information management apparatus100has a HDD (hard disk drive)120as main hardware. In addition, the information management apparatus100has an input/output control unit101, a characteristic-amount extraction unit102, a registration unit103, a deletion unit104, a retrieval unit105, a similarity calculation unit106, and an index generation unit107as main software programs. Each of these units is loaded on a RAM110and causes a CPU (not shown) to execute processing for implementing its function.

The HDD120stores an image-data storage unit121, a metadata DB (Data Base)122, a characteristic-amount DB123, a similar-information list DB124, and a characteristic-amount index125. Note that the storage unit is not limited to a HDD, but it may be of any known types such as an optical disk and a memory card.

The image-data storage unit121stores image data. In order to store image data, any known methods may be employed; the image data may be stored on a file system provided by an OS (Operating system) or stored on an image database in which the image data are stored.

The metadata DB122stores metadata representing attributes of image data.FIG. 2shows an example of the data structure of the metadata DB122. As shown inFIG. 2, the metadata DB122stores image IDs and image file names as examples of the metadata of image data so as to be associated with each other.

Note that the metadata are not limited to the image file names, but they may be of any information so long as they represent the attributes of image data.

The image file names are used to identify which part of the image-data storage unit121images are stored. Note that image data may be stored in an external system and the image file names may contain information for identifying the external system. For example, in order to manage image information stored in the external system, the image data may be stored in an external server on the Internet and the image file names may be represented by a URL (Uniform Resource Locator).

The characteristic-amount DB123stores the characteristic amounts of images stored in the image-data storage unit121.FIG. 3shows an example of the data structure of the characteristic-amount DB123. As shown inFIG. 3, the characteristic-amount DB123stores image IDs and the characteristic amounts of the images so as to be associated with each other.

As examples of the characteristic amounts of images, vector data such as the color histogram of the images are available.FIG. 3shows a case in which the vector data as the characteristic amount of the image identified as “image ID=1” are represented by [a1, a2, . . . , an] and the vector data as the characteristic amount of the image identified as “image ID=2” are represented by [b1, b2, . . . , bn]. Note that as the characteristic amounts of images, any known characteristic amounts such as coloring, color distribution, composition, and patterns can be stored. Note that the characteristic amounts are not necessarily different in form from original information (images in the embodiment). If it is possible to determine the similarity between images with the original information, the original information itself may be handled as the characteristic amounts.

The similar-information list DB124stores similar information whose characteristic amounts are determined to be similar to each other among images stored in the image data storage unit121.FIG. 4shows an example of the data structure of the similar-information list DB124. As shown inFIG. 4, the similar-information list DB124stores image IDs and similar-information lists that include at least a group of similar image IDs as the image IDs of similar images and the similarity, so as to be associated with each other. A method for calculating the similarity is described below.

FIG. 5shows another example of the data structure of the similar-information list DB124. As shown inFIG. 5, the similar-information list DB124stores image IDs, similar image IDs, and the similarity so as to be associated with each other. Even if the similar-information list DB124is configured in this manner, it can store plural similar information for one image.

Note that in this case, information, which includes at least a group of the similar image ID and the similarity each associated with the same image ID, corresponds to the similar-information list according to the embodiment. In the example ofFIG. 5, the similar-information list associated with “image ID=1” is the information that includes a group of 2 for the similar image ID and 0.10 for the similarity and a group of 10 for the similar image ID and 0.11 for the similarity.

Once similar information is registered in advance in the similar-information list DB124, it is not necessary to calculate the similarity between characteristic amounts to retrieve the similar information at the time of retrieving a similar image, thereby making it possible to accelerate retrieval processing.

The characteristic-amount index125is a space index that classifies characteristic amounts included in the characteristic-amount DB123into pieces. The characteristic amounts are classified by the division of a vector space (characteristic-amount space) of the characteristic amounts. For example,FIG. 6shows a division example of the characteristic-amount space based on the VP-tree (Vantage Point tree).

FIG. 6shows a characteristic-amount space300in two dimensions (Note that it is actually multidimensional). InFIG. 6, respective points represent characteristic amounts. Arcs represent boundary lines by which the characteristic-amount space300is divided. In the division of the characteristic-amount space300, the number of the characteristic amounts is divided in half. For example, the characteristic-amount space300is divided into two pieces of partial spaces by an arc A1. Then, one of the partial spaces divided by the arc A1is further divided in half by an arc A2. Through the repetition of such a division, the partial spaces shown inFIG. 6are formed. Note that in the characteristic-amount space300, the similarity between two characteristic amounts corresponds to the distance between them.

The relationship between the partial spaces and the characteristic amounts shown inFIG. 6can be represented by a tree structure (binary tree). Information representing this tree structure corresponds to the characteristic-amount index125.

FIG. 7shows a configuration example of the characteristic-amount index125. InFIG. 7, nodes Nn (n is a positive integer) represent the characteristic-amount spaces, and nodes En (n is a positive integer) represent the characteristic amounts. Assume that a node N1is the characteristic-amount space300inFIG. 6, and nodes N2and N3represent the partial spaces divided by the arc A1. In addition, nodes N4and N5represent the partial spaces divided by the arc A2. Note, however, thatFIG. 7does not conform toFIG. 6in every respect.

Among the nodes corresponding to the partial spaces in such a tree structure, terminal nodes are the partial spaces of a minimum unit. In other words, the partial spaces of the minimum unit have only one partial space. As shown inFIG. 7, the nodes En representing the characteristic amounts belong to the nodes as the partial spaces of the minimum unit. Note that the term just described as the “partial space” in the following description refers to the partial space of the minimum unit.

By tracing the tree structure, the characteristic-amount index125configured by such information can reduce a calculation amount required for searching for the partial space to which a certain characteristic amount belongs. This is based on the same principle as general binary-tree search methods.

The input/output control unit101controls input processing for registering and retrieving images specified by the user via the client PC and processing for outputting retrieval results to the client PC.

The characteristic-amount extraction unit102extracts the characteristic amount from image data. As a method for extracting the characteristic amount, any known methods including one described in Patent Document 1 in which the histogram of a color is extracted as the characteristic amount can be employed. Note that when original information is handled as the characteristic amount itself, the characteristic-amount extraction unit102is not required.

The registration unit103executes, in response to a request for registering image data, processing for registering the data in the image-data storage unit121, the metadata DB122, the characteristic-amount DB123, the similar-information list DB124, and the characteristic-amount index125.

The deletion unit104executes, in response to a request for deleting image data, processing for deleting the data from the image-data storage unit121, the metadata DB122, the characteristic-amount DB123, the similar-information list DB124, and the characteristic-amount index125.

The similarity calculation unit106compares the characteristic amount of information to be registered, which is extracted by the characteristic-amount extraction unit102, with that of existing information registered in the characteristic DB123, and calculates the similarity for indicating to what extent they are similar to each other.

For example, assume that the vector data of the characteristic amounts to be compared are [a1, a2, . . . , an] and [b1, b2, . . . , bn], the similarity calculation unit106calculates the similarity S based on the following formula (1).
S=Σ|ai−bi|(1)

The similarity S is calculated based on the sum of the absolute values of differences between the components of the vector data of the characteristic amounts. Therefore, the smaller the similarity S is, the higher the similarity between the characteristic amounts becomes.

The elements of the vector data are generally expressed in the form of floating points. Furthermore, the vector data as the characteristic amounts of image data are generally data of one hundred dimensions through two hundred dimensions, and their data size are 1.5 k through 2.0 k bytes. Accordingly, processing for calculating the similarity expressed by the above formula (1) is arithmetic processing that causes a heavy load.

The index generation unit107updates the characteristic-amount index125based on the characteristic amounts of image data at the time of registering the image data.

The retrieval unit105mainly retrieves characteristic amounts similar to the characteristic amount of image data serving as a seed for retrieval (retrieval conditions) using the characteristic-amount index125and the similar-information list DB124. In addition, the retrieval unit105retrieves data concerning image data according to retrieved characteristic amounts from the image-data storage unit121, the metadata DB122, etc., as occasion demands.

Next, a processing procedure in the information management apparatus100is described, starting from similar-image retrieval processing. In describing the retrieval processing,FIG. 8is first used.

FIG. 8illustrates the similar-image retrieval processing on a conceptual basis. InFIG. 8, a rectangle denoted by reference numeral400shows the characteristic-amount space in the characteristic-amount index125, which is expressed on a two-dimensional space. Note thatFIG. 8is different fromFIG. 6in that partial spaces are divided by straight lines, but it is for the sake of convenience. InFIG. 8, a point S represents the characteristic amount (hereinafter referred to as a “characteristic amount S”) of image data (serving as a seed for retrieval) (hereinafter referred to as a “seed image”) provided as a retrieval condition. Points other than the characteristic amount S represent the characteristic amounts classified into the partial spaces in the characteristic-amount index125. A circle c1shown by a solid line located around the point S is a retrieval circle formed based on the similarity (hereinafter referred to as a “similarity condition”) provided as the retrieval condition. In other words,FIG. 8illustrates an example for searching for (retrieving) a characteristic amount included in the retrieval circle c1. Note, for example, that when the width of the characteristic-amount space400is “1.0” and the height thereof is “1.0,” a value specified as the similarity condition could be greater than 0 and smaller than 1.0. For example, when the value “0.2” is provided as the similarity condition, the retrieval circle c1has a radius of 0.2.

In retrieving the characteristic amount included in the retrieval circle c1, the information management apparatus100first specifies a partial space401to which the characteristic amount S belongs using the characteristic-amount index125. Then, the information management apparatus100calculates the similarity (distance) between the characteristic amount S and each of characteristic amounts401a,401b, and401c(hereinafter referred to as “characteristic amounts401” when they are collectively named) included in the partial space401, and determines whether they exist in the retrieval circle c1. In other words, the information management apparatus100determines whether each of the characteristic amounts401exists in the retrieval circle c1using the calculated similarity and the similarity provided as the retrieval condition. The information management apparatus100holds the image ID and the similarity corresponding to the characteristic amount (characteristic amount401binFIG. 8) existing in the retrieval circle c1as a retrieval result.

Meanwhile, although the partial space401to which the characteristic amount S belongs can be specified at high speed based on the characteristic-amount index125, the range of the retrieval circle c1straddles partial spaces other than the partial space401. Accordingly, the characteristic amount included in the retrieval circle c1(i.e., characteristic amount satisfying the retrieval condition) could exist even in the partial spaces other than the partial space401. Therefore, there is a high possibility that a retrieval result at this moment includes retrieval omissions. In order to deal with this problem, the information management apparatus100performs the following processing to retrieve the characteristic amount included in the retrieval circle c1from among those belonging to other partial spaces with a least calculation amount.

The information management apparatus100first acquires characteristic amounts included in the similar-information list of the similar-information list DB124for each of the characteristic amounts401. In this processing, in other words, the characteristic amounts included in circles shown by broken lines located around each of the characteristic amounts401inFIG. 8are acquired. Accordingly, characteristic amounts402a,402b,402c,402d, and402e(hereinafter referred to as “characteristic amounts402” when they are collectively named) are acquired. Note that the information management apparatus100may acquire only the characteristic amounts whose associated similarity is smaller than or equal to the similarity (0.2 in the embodiment) provided as the retrieval condition instead of acquiring all the characteristic amounts included in the similar-information list. Such a configuration can attain the acceleration of processing speed.

The information management apparatus100calculates the similarity between the characteristic amount S and each of the characteristic amounts402, and determines whether they exist in the retrieval circle c1. The information management apparatus100holds the image ID and the similarity corresponding to the characteristic amount (characteristic amount402binFIG. 8) existing in the retrieval circle c1as a retrieval result.

Next, the information management apparatus100also acquires characteristic amounts included in the similar-information list DB124corresponding to the characteristic amounts402. Accordingly, inFIG. 8, characteristic amounts403a,403b,403c,403d,403e, and403f(hereinafter referred to as “characteristic amounts403” when they are collectively named) included in circles shown by dashed lines located around each of the characteristic amounts402are acquired. The information management apparatus100calculates the similarity between the characteristic amount S and each of the characteristic amounts403, and determines whether they exist in the retrieval circle c1. The information management apparatus100holds the image ID and the similarity corresponding to the characteristic amount (characteristic amount403cinFIG. 8) existing in the retrieval circle c1as a retrieval result.

Then, the information management apparatus100also acquires characteristic amounts included in the similar-information list DB124corresponding to the characteristic amounts403. Accordingly, inFIG. 8, characteristic amounts404a,404b,404c, and404d(hereinafter referred to as “characteristic amounts404” when they are collectively named) included in circles shown by dotted lines located around each of the characteristic amounts403are acquired. The information management apparatus100calculates the similarity between the characteristic amount S and each of the characteristic amounts404, and determines whether they exist in the retrieval circle c1. In this case, no characteristic amounts404exist in the retrieval circle c1. Therefore, the information management apparatus100ends the similar-image retrieval processing. Accordingly, in the example ofFIG. 8, the image IDs and the similarities corresponding to the characteristic amounts401b,402b, and403care output as a retrieval result.

Thus, the characteristic amounts recursively acquired using the similar-information list are regarded as objects to be determined for calculating the similarity between the characteristic amounts involved and the characteristic amount S. As a result, in partial spaces other than the partial space401, the characteristic amounts included in other partial spaces adjacent to the partial spaces can also be regarded as the objects to be determined for calculating the similarity between the characteristic amounts involved and the characteristic amount S. Accordingly, it is possible to reduce the number of retrieval omissions and accelerate the retrieval processing.

Here, a processing content is specifically described using a flowchart.FIG. 9is the flowchart for explaining the similar-image retrieval processing.

In step S101, the input/output control unit101receives the seed image and the similarity condition from the client PC. The seed image and the similarity condition are specified or input by the user via the user I/F201. Note, here, that the seed image specified may not be registered in the image management apparatus100. Furthermore, instead of being received by the client PC, the similarity condition may be set in advance in the HDD120of the information management apparatus100.

Next, the characteristic-amount extraction unit106extracts the characteristic amount of the seed image from the seed image (i.e., it generates the characteristic amount of the seed image based on the seed image) (S102). Then, the retrieval unit105specifies a partial space into which the characteristic amount S is classified using the characteristic-amount index125(i.e., it determines which partial space the characteristic amount S belongs to) (S103).

Next, the retrieval unit105acquires image IDs associated with the specified partial space from the characteristic-amount index125, acquires characteristic amounts corresponding to the image IDs from the characteristic-amount DB123, and initializes a group of object characteristic amounts (group of image IDs and characteristic amounts regarded as objects to be determined for calculating the similarity between the characteristic amounts involved and the characteristic amount S) with the acquired characteristic amounts and their image IDs (S104).

Then, the similarity calculation unit106calculates the similarity (distance) between the characteristic amount S and all the characteristic amounts included in the group of object characteristic amounts (S105). Next, the retrieval unit105determines whether the calculated similarity is smaller than or equal to the similarity specified as a similarity condition (S106). If there is any characteristic amount satisfying the similarity condition (Yes in step S106), the retrieval unit105adds the image ID and the similarity of the characteristic amount satisfying the similarity condition to a retrieval result (S107).

Then, the retrieval unit105saves the group of object characteristic amounts as a group of searched characteristic amounts (S108). The group of searched characteristic amounts comprises the characteristic amounts and their image IDs that have been subjected to the determination for calculating the similarity between the characteristic amounts involved and the characteristic amount S. Next, the retrieval unit105acquires the similar-information lists corresponding to the characteristic amounts included in the group of searched characteristic amounts from the similar-information list DB124based on the image IDs of the characteristic amounts, acquires the characteristic amounts corresponding to the similar image IDs (image IDs) included in the acquired similar-information lists from the characteristic-amount DB123, and updates the group of object characteristic amounts with the acquired characteristic amounts and their image IDs (S109).

Then, the retrieval unit105removes overlaps in the group of object characteristic amounts (S110). This is because if there is any characteristic amount overlapped in plural similar-information lists, two or more of the same characteristic amounts exist in the group of object characteristic amounts. The removal of the overlaps prevents unnecessary calculation of the characteristic amounts: the same characteristic amounts are not allowed to be used repeatedly for calculating the similarity between the characteristic amounts involved and the characteristic amount S.

Next, the retrieval unit105removes the characteristic amounts and their image IDs included in the group of searched characteristic amounts from the group of object characteristic amounts (S111). This prevents unnecessary calculation of the characteristic amounts: the characteristic amounts, which have been determined to calculate the similarity between the characteristic amounts involved and the characteristic amount S, are not allowed to be used repeatedly for calculating the similarity.

Then, the processing steps after S105are executed based on a group of new object characteristic amounts. In other words, the characteristic amounts included in the similar-information lists are recursively determined to calculate the similarity and determined to find whether the characteristic amounts satisfy the similarity condition.

If there is no characteristic amount satisfying the similarity condition among those included in the group of object characteristic amounts during the recursive processing (No in step S106), the retrieval unit105leaves the loop processing (recursive processing).

However, the end condition for the loop processing is not limited to this. For example, the retrieval unit105may leave the loop processing after conducting the processing steps of S107through S111only once. In this case, determination objects for calculating the similarity are the characteristic amounts belonging to the same partial space as the characteristic amount S and other characteristic amounts similar to the characteristic amounts. Furthermore, the retrieval unit105may just leave the loop processing when the number of repeated processing times reaches a prescribed value. In this case, there is a high possibility of omissions in a retrieval result, but high-speed retrieval is possible. Furthermore, the retrieval unit105may leave the loop processing when a status where no characteristic amount exists in the similarity condition (retrieval circle) reaches a prescribed number of times or is continued for a predetermined number of times. As a result, it is possible to reduce the possibility of retrieval omissions and perform the retrieval processing at a constant high speed.

Next, the retrieval unit105acquires the image file name corresponding to the image ID included in the retrieval result from the metadata DB122(S112). Then, the retrieval unit105acquires the image data corresponding to the acquired image file name from the image-data storage unit121and sorts the acquired image data based on the similarity (S113). Note that when image data are stored in an external system, the retrieval unit105acquires image data corresponding to the image file name from the external system. Next, the input/output control unit101transmits the acquired image data to the user I/F201of the client PC (S114). The user I/F201of the client PC displays the received image data on a display unit. Note that in step S114, a list of metadata (image file names) may be transmitted to the user I/F201instead of the image data. In this case, the list of the metadata is displayed on the client PC. The input/output control unit101may transmit image data selected from the list to the client PC.

As described above, the information management apparatus100according to the embodiment specifies the partial space in the space index to which the seed image belongs, and regards the characteristic amounts belonging to the partial space and other characteristic amounts similar to the characteristic amounts as objects to be determined for calculating the similarity between the characteristic amounts involved and the seed image. Accordingly, compared with a case in which all the characteristic amounts registered in the characteristic-amount DB123are regarded as objects to be determined for calculating the similarity, it is possible to greatly reduce a calculation amount. The degree of reduction in the calculation amount differs depending on accumulated characteristic amounts and the number of dimensions of the characteristic amounts. However, in consideration of a fact in which the characteristic amounts of image data are generally data of one hundred dimensions through two hundred dimensions, such a degree is remarkable. Accordingly, the information management apparatus100can accelerate the similar-image retrieval processing for an unregistered seed image.

Furthermore, the information management apparatus100also calculates the similarity between the seed image and the characteristic amounts recursively acquired based on the similar-information lists and determines as to whether the characteristic amounts satisfy the similarity condition. Accordingly, even if the range of the similarity condition (retrieval circle) straddles partial spaces, it is possible to reduce the number of retrieval omissions.

Note that the form of the space index (characteristic-amount index125) is not limited to a predetermined one. Besides the VP-tree described above, various known space indexes based on the R-tree, the M-tree index, etc., can be employed.

In other words, simply by specifying the partial space in the space index of any form into which the characteristic amount of the seed image is classified, it is possible to calculate the similarity between the seed image and the characteristic amounts included in the partial space and between the seed image and other characteristic amounts included in the similar-information lists of the characteristic amounts, thereby making a determination as to whether the characteristic amounts involved satisfy the similarity condition.

However, in the case of the space index based on the M-tree, partial spaces are overlapped with each other. Accordingly, a plurality of the partial spaces, into which the characteristic amount of the seed image is classified, could exist. In this case, however, it is only necessary to execute the processing described inFIG. 9for the plural partial spaces. Note that the index methods of characteristic amounts are specifically described, for example, in JP-A-2000-112973.

Next, a processing procedure at the time of registering image information is described.FIG. 10is a flowchart for explaining image-information registration processing.

First, the input/output control unit101receives a registration image file and its metadata (such as a file name) from the client PC (S201). The registration image file is specified or selected by the user via the user I/F201in the client PC. Then, the registration unit103stores the registration image file in the image-data storage unit121and registers the image ID of the registration image file and the image file name thereof in the metadata DB122so as to be associated with each other (S202).

Next, the characteristic-amount extraction unit102reads image data (registration image) from the registration image file (S203) and extracts (generates) the characteristic amount of the registration image (S204). The extracted characteristic amount is output as vector data as described above. Then, the index generation unit107registers the image ID of the registration image file in the characteristic-amount index125based on the characteristic amount of the registration image (S205).

Next, the information management apparatus100executes the similar-image retrieval processing for the registration image (S206). This retrieval processing is executed in accordance with the processing steps shown inFIG. 9. Note, here, that the registration image is used as the seed image, and the similarity (for example, 0.3, etc.) set in advance in the HDD120is used as the similarity condition.

Then, the registration unit103registers a group of the image ID (similar image ID) output by the similarity-image retrieval processing and the similarity in the similar-information list DB124as a similar-information list (S207). Next, the information management apparatus100executes the similar-information-list update processing with respect to registered image files (S208). The similar-information-list update processing is specifically described below.

Then, the registration unit103registers the characteristic amount extracted by the characteristic-amount extraction unit102in step S204in the characteristic-amount DB123(S209) and ends the registration processing.

Thus, according to the embodiment, a comparison with all the characteristic amounts is not made in the creation of the similar-information list, but a similar image is retrieved by the similar-image retrieval processing described inFIG. 9. Accordingly, it is possible not only to reduce processing load in the registration processing but also to accelerate the registration processing that involves the creation of the similar-information list.

Note that in the above example, the registration of the image information is executed at the same time as the retrieval of the similar image and the registration of the similar-information list. However, the retrieval of the similar image and the registration of the similar-information list may be executed after the completion of the processing shown inFIG. 10only before the retrieval processing is executed.

Here, the similar-information-list update processing in step S208ofFIG. 10is specifically described. Newly-registered image information and its corresponding similar information (existing information) are information items similar to each other. Accordingly, it is necessary to add the newly-registered image information to the similar-information lists of the existing information. This processing is executed in the similar-information-list update processing.

FIG. 11is a flowchart for explaining the similar-information-list update processing.

First, the retrieval unit105acquires a similar image ID from the similar-information lists of registration images (S301). Next, the retrieval unit105acquires a similar-information list corresponding to the acquired similar image ID from the similar-information list DB124(S302). Then, the registration unit103adds similar information, in which the ID of the image to be registered and the similarity output in step S206are associated with each other, to the acquired similar-information list (S303).

Next, the retrieval unit105determines whether all the data in the similar-information lists of the registration images have been processed (S304). If all the data have not been processed (No in S304), the retrieval unit305acquires the next similar image ID and repeats the processing (S301).

If all the data have been processed (Yes in S304), the retrieval unit105ends the similar-information-list update processing.

Here, information deletion processing by the information management apparatus100is described.FIG. 12is a flowchart for explaining the deletion processing.

First, the input/output control unit101displays a list of image file names registered in the metadata DB122on the user I/F201of the client PC (S401) and receives instructions from the user as to which image is to be deleted (S402).

Next, the retrieval unit105acquires the similar-information list of the image selected by the user from the similar-information list DB124(S403).

Then, the retrieval unit105executes similar-information-list deletion processing (S404). The similar-information-list deletion processing is specifically described below.

Next, the deletion unit104deletes the image file of a deletion image from the image-data storage unit121and deletes its metadata from the metadata DB122(S405). In addition, the deletion unit104deletes the characteristic amount of the deletion image from the characteristic amount DB123(S406) and deletes the similar-information list of the deletion image from the similar-information list DB124(S407).

Then, the deletion unit104deletes the image ID of the deletion image from the characteristic-amount index125(S408) and ends the deletion processing.

Here, the similar-information-list deletion processing in step S404is specifically described.FIG. 13is a flowchart for explaining the similar-information-list deletion processing.

First, the retrieval unit105acquires a similar image ID from the similar-information list (S501). Next, the retrieval unit105acquires a similar-information list corresponding to the acquired similar image ID from the similar-information list DB124(S502). Then, the deletion unit104deletes similar information having the similar information ID matching the deletion image ID from the acquired similar-information list (S503). Next, the retrieval unit105determines whether all the data in the similar-information list of the deletion image have been processed (S504). If all the data have not been processed (No in S504), the retrieval unit105acquires the next similar image ID and repeats the processing (S501).

If all the data have been processed (Yes in S504), the retrieval unit105ends the similar-information-list deletion processing.

Thus, the information of the deletion image is deleted using the similar-information list124and the characteristic-amount index125in the deletion processing. Accordingly, it is possible to prevent the retrieval of image data whose image file does not exist in the image-data storage unit121in the subsequent similarity-image retrieval processing.

Note that in the embodiment, the similar-information list DB124is an example of a characteristic-amount association unit, the characteristic-amount index125is an example of a space index management unit, the processing step S103by the retrieval unit105is an example of a partial space determination unit, and each of the processing steps S105through S111by the retrieval unit105is an example of a similarity determination unit.

The present application is based on Japanese Priority Application No. 2007-277763 filed on Oct. 25, 2007, the entire contents of which are hereby incorporated herein by reference.