WO2004047026A1 - Programme de recherche d'images - Google Patents

Programme de recherche d'images Download PDF

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Publication number
WO2004047026A1
WO2004047026A1 PCT/JP2002/012135 JP0212135W WO2004047026A1 WO 2004047026 A1 WO2004047026 A1 WO 2004047026A1 JP 0212135 W JP0212135 W JP 0212135W WO 2004047026 A1 WO2004047026 A1 WO 2004047026A1
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WO
WIPO (PCT)
Prior art keywords
image
image data
similarity
feature
search
Prior art date
Application number
PCT/JP2002/012135
Other languages
English (en)
Japanese (ja)
Inventor
Yusuke Uehara
Daiki Masumoto
Shuichi Shiitani
Susumu Endo
Takayuki Baba
Original Assignee
Fujitsu Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fujitsu Limited filed Critical Fujitsu Limited
Priority to PCT/JP2002/012135 priority Critical patent/WO2004047026A1/fr
Priority to JP2004553119A priority patent/JP4160050B2/ja
Priority to CNB02829713XA priority patent/CN100346339C/zh
Publication of WO2004047026A1 publication Critical patent/WO2004047026A1/fr
Priority to US11/124,107 priority patent/US20050210019A1/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/53—Querying
    • G06F16/532—Query formulation, e.g. graphical querying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries

Definitions

  • the present invention relates to an image retrieval program for retrieving desired image data from an image database based on key image data, and more particularly to an image retrieval program capable of improving retrieval accuracy.
  • a powerful image search device uses a similar image search technology, which numerically calculates the degree of similarity between key image data and the image data to be searched.
  • the feature amount is defined. This feature is used for calculating the similarity of the images.
  • the feature amount includes the color layout and texture of the image (in the case of graphics, etc., the pattern attached to the surface of the figure and the drawing for expressing the texture) and the image
  • the conventional image search device searches for similar images from various viewpoints.
  • a plurality of types of feature values are prepared, and after the user specifies the type of feature value that suits the purpose, the search is executed.
  • the content of the target image data must satisfy certain conditions that are determined for each type of feature. Needed.
  • the search target is a collection of image data in which image data of various contents are mixed, such as image data collected using the Internet that has become widespread in recent years, the condition of the feature type will not be met. There is also a problem that a large amount of image data is included and the search accuracy is reduced.
  • the present invention has been made in view of the above, and an object of the present invention is to provide an image search program capable of improving search accuracy. Disclosure of the invention
  • the present invention provides a computer comprising: a feature amount extracting unit configured to extract a plurality of types of feature amounts from a key image; and a type of each feature amount extracted by the feature amount extracting unit.
  • a similarity calculating unit that calculates a similarity between the key image and each image to be searched, and a search result output unit that outputs an image corresponding to the similarity as a search result. It is an image search program.
  • a force for calculating a similarity degree that matches a human similarity is determined as whether or not there is compatibility. Based on this, the similarity between the key image and each image to be searched is calculated, and the image corresponding to the similarity is output as a search result. This prevents mis-selection of these types, and improves search accuracy.
  • FIG. 1 is a block diagram showing a configuration of an embodiment according to the present invention.
  • FIG. 2 is a diagram showing a table structure of an image database 110 shown in FIG.
  • FIG. 4 is a diagram showing a table structure of a feature amount database 120 shown in FIG. 1
  • FIG. 4 is a flowchart for explaining an operation of the same embodiment
  • FIG. FIG. 6 is a flowchart for explaining the feature amount data storage processing shown in FIG. 6.
  • FIG. 6 is a flowchart for explaining the search processing shown in FIG. 4
  • FIG. 7 is a flowchart showing the compatibility determination shown in FIG.
  • FIG. 8 is a flow chart for explaining the processing
  • FIG. 8 is a view for explaining the search processing shown in FIG. 6, and FIG.
  • FIG. 9 is a view for explaining the suitability judgment regarding the color layout feature amount in the embodiment.
  • FIG. 10 shows the colors in the same embodiment.
  • FIGS. 11A and 11B are diagrams for explaining the suitability determination relating to the art feature
  • FIG. 11 is a diagram illustrating the compatibility determination regarding the color layout feature in the same embodiment
  • FIGS. 12A and 12B are the same embodiment.
  • FIG. 13 is a diagram illustrating a color layout feature quantity in FIG. 13.
  • FIG. 13 is a diagram showing a calculation formula used for determining suitability of the color layout feature quantity in the same embodiment.
  • FIG. 15 is a diagram for explaining the suitability judgment regarding the texture feature amount in the embodiment.
  • FIG. 15 is a diagram for explaining the suitability judgment regarding the shape feature amount in the same embodiment.
  • FIG. 11 shows the colors in the same embodiment.
  • FIGS. 11A and 11B are diagrams for explaining the suitability determination relating to the art feature
  • FIG. 11 is a diagram illustrating the compatibility determination
  • FIG. 16 shows a search result screen 300 when the color layout feature is used in the same embodiment.
  • FIG. 17 shows a texture feature in the same embodiment.
  • FIG. 18 is a diagram showing a search result screen 310 when using a shape feature, and
  • FIG. 18 is a diagram showing a search result screen 320 when using a shape feature in the same embodiment.
  • FIG. 9 is a block diagram showing a configuration of a modification of the same embodiment.
  • FIG. 1 is a block diagram showing a configuration of an embodiment according to the present invention.
  • the image retrieval apparatus 100 shown in FIG. 1 includes a plurality of feature amounts (color layouts) used to calculate the similarity between key image data and image data in an image database 110 described later.
  • This is a device that selects a feature value that is highly likely to match human similar sensations from among the feature values, texture features, shape features, etc.), and performs an image search using this feature value.
  • the input unit 101 is an input device used for inputting key image data as a search key for similar image search and various key inputs, and includes a digital camera, a scanner, and an Internet communication device. Use a device, keyboard, mouse, etc.
  • the display unit 102 is an LCD (Liquid Crystal Display), CRT (Cathode Ray Tube), or the like, and displays a key image data and similarity on a search result screen (see FIGS. 16 to 18) related to image search.
  • a predetermined number of search results (image data) are displayed to the user in descending order of the number of search results.
  • the image database 110 is a database that is configured on a magnetic storage device or a semiconductor memory and stores image data to be searched. Specifically, as shown in FIG. 2, the image database 110 has fields of an image ID and an address.
  • the image ID is an identifier for identifying each image data and key image data to be searched.
  • the address is where the image data is actually stored Represents the place where you are.
  • the image data whose image ID is “0000001” corresponds to the image data 111 to be searched shown in FIG. 8 (b).
  • This image data 111 # represents a wallpaper image with vertical stripes.
  • the image data of the image ID is "0000002" corresponds to the image data 111 2 to be searched shown in FIG. 8 (c).
  • the image data 111 2 the same procedure as above Symbol image data 11 represents the wallpaper image of stripes.
  • the image data of the image ID is "0000003" corresponds to the image data 111 3 to be searched shown in FIG. 8 (d).
  • the image data 111 3 represents the photographic images obtained by photographing the trees and the house of the forest.
  • Feature database 120 the image data stored in the image database 110 (see FIG. 2) (for example, image data 111 ⁇ : L ll 3 (FIG. 8 (b) ⁇ (d) see)) or, the key image It is a database that stores feature data that numerically represents each feature of data (for example, key image data 200 (see FIG. 8 (a))).
  • the following three types (1) to (3) are illustrated as types of image feature amounts.
  • the (1) color layout feature quantity is a feature quantity representing a spatial distribution state of colors on image data.
  • the texture feature amount is a feature amount representing a pattern in image data or a drawing for representing a texture.
  • the shape feature is a feature representing the shape of the contour of the object existing in the image data.
  • the feature amount database 120 includes a color layout feature amount data table 121, a texture feature amount data table 122, and a shape feature amount data table 123.
  • Each of these color rate feature data table 121, texture feature data table 122, and shape feature data table 123 has fields of image ID and feature data.
  • the image ID corresponds to the image ID in the image database 110 (see Fig. 2).
  • the feature data in the color layout feature data table 122 is data representing a color layout feature related to image data corresponding to the image ID.
  • the feature amount data of the texture feature amount data table 122 is data representing a texture feature amount regarding the image data corresponding to the image ID.
  • the feature amount data of the shape feature amount data table 123 is data representing a shape feature amount regarding image data corresponding to the image ID.
  • the feature amount data extraction unit 103 extracts the above-described three types of feature amounts (color layout feature amount, color layout feature amount, The feature amount data corresponding to the texture feature amount and the shape feature amount) are extracted and stored in the feature amount database 120 (see FIG. 4).
  • These feature data are used to calculate the similarity between the key image data and the image data to be searched.
  • the suitability determination unit 104 calculates the similarity of the feature data used to numerically calculate the similarity between the key image data and the image data to be searched. Whether or not the similarity matching the similar sensation (similarity) of the human being is calculated is determined by whether or not the feature data (feature) is compatible with the given key image data (image data). Is determined.
  • the similarity calculation unit 105 calculates a vector value corresponding to the feature value data of the key image data determined to be compatible and a feature value data of each image data to be searched.
  • the search unit 106 calculates the similarity of each piece of image data to be searched with respect to the key image data based on the Euclidean distance from the vector value.
  • step S A2 it is determined whether or not the user has a request for image search, and in this case, the determination result is “No”. Thereafter, the determination in step S A1 and step S A2 is repeated until the determination result in step S A1 or S A 2 becomes “Y e s”.
  • step S if there is a request from the user for storing the feature data, step S
  • step SA3 from the image data stored in the image database 110, feature amount data corresponding to the above three types of feature amounts (color layout feature amount, texture feature amount, and shape feature amount) are extracted. A feature amount data storage process for storing these in the feature amount database 120 is executed.
  • the feature data extraction unit 103 extracts one image data (for example, image data 1) from the image database 110 (see FIG. 2). 1 (See Fig. 8 (b))).
  • the feature data extraction unit 103 corresponds to three types of features (color layout features, texture features, and shape features) from the image data acquired in step SB1. Extract feature data.
  • the color layout features are obtained by arranging the average of the color values of each partial image data I one-dimensionally when the image data is divided vertically and horizontally into an appropriate number of grids. It is expressed as a value.
  • the average of the color values of the partial image data I 5 j is represented by a three-dimensional vector having components of R (red), G (green), and B (blue) in the RGB color space.
  • the image data contains a color that is included at a certain percentage or more
  • Pixels having colors satisfying the condition (1A) are spatially concentrated. These two conditions (1A) and (2A) are based on the average of the color values of the partial image data. This is a condition for indicating that colors are approximated. For example, if many colors exist little by little in image data, averaging the color values is not a good approximation. '
  • condition (1A) is that pixels are spatially concentrated in the image data. Whether or not the above conditions (1 ⁇ ) and (2 ⁇ ) are satisfied is determined by the following method. '(How to determine condition (1A))
  • the RGB color space is divided into partial color spaces.
  • a ratio value of the number of pixels of the calculation result to the total prime number of the partial image is calculated for each partial color space.
  • That partial color space is The corresponding color value is set as a representative color of the partial image for the partial image.
  • a method of determining the representative color of the partial image for example, there is a method of determining a color at the position of the center of gravity of the corresponding partial color space. If the representative color of this partial image exists, the condition (1
  • the concentration is calculated by the following method. First, as shown in Fig. 12, for all the pixels on the partial image ⁇ , a window m with the size of w X h is applied so that the pixels are at the center, and the partial image in the window m is The number RCxy of pixels corresponding to the representative color is counted. At this time, the degree of concentration SC is calculated by the equation (1) shown in FIGS. In equation (1), RC is the total number of pixels corresponding to the representative color of the partial image, and N is a predetermined value.
  • the concentration SC has a representative color of a partial image that is equal to or greater than a predetermined value
  • the condition (2A) is satisfied.
  • image data that is not so may show higher similarity than data.
  • key image data 200 shown in FIG. 8 (a) is given as a key image for image search.
  • FIG. 8 (b) How the search target, FIG. 8 (b), and an image data 1 1, 1 1 1 2 and 1 1 1 3 shown in (c) and (d).
  • Key image data 2 0 0 and the image data 1 1 1 3 is a photographic image obtained by photographing a landscape where there is a house in the grove.
  • the image data 1 1 1 i and 1 1 1 2 correspond to a wallpaper image covered with a uniform texture.
  • the texture features are calculated using the well-known Tamura method (H. Tamura, S. Mori, and T. Yamawaki, lexture Features Corresponding Visual Perception, IEEE Trans. System Man and Cybernetics, Vol. 8, No. 6, (1978). ) Calculated by.
  • texture features are represented by three components: “coarseness”, “contrast” J, and “directionality”, and the degree of each component is represented by a three-dimensional numerical value. It is extracted from image data as a vector.
  • “Roughness” indicates the size of the scale of the pattern appearing in the image data, and the larger the scale, the larger the value.
  • “Contrast” indicates the degree of variation in luminance value, and the larger the variation, the larger the value.
  • Directional indicates the degree to which the direction of the edge component in the image data is concentrated in a certain direction. The larger the frequency in the direction with the highest frequency among the directions of the edge component, the larger the value. It becomes.
  • the key image data 200 is composed of "roughness” and “contrast”. ”And“ direction ”, the image data 1 1 1 and 1 1 1 2 show a higher similarity than the image data 1 1 1 3 .
  • the user by the image search is looking for landscape images, it is inappropriate for the image data 1 1 1 and 1 1 1 2 is included in the search results. If such inappropriate image data is ranked higher in the search results, more unnecessary image data will be seen in order to find the target image data, resulting in lower search efficiency. Will be.
  • the uniformity of the texture is calculated, and if the uniformity is smaller than a predetermined value, it is determined that there is no suitability, and the texture feature can not be used in the search. .
  • image data key image data 200, etc.
  • image data is divided into four equal parts vertically and horizontally.
  • a texture feature is extracted as a vector from each of the divided partial images.
  • the method of calculating the texture feature may be the same as the method of calculating the feature used in the similarity calculation, or may be another method.
  • the feature of the three-dimensional vector is extracted from each partial image.
  • the uniformity of the texture can be represented by the degree of variation of the extracted characteristic vectors.
  • the variance is calculated as the degree of variation for the four feature vectors obtained from the partial images, and if the variance is larger than a predetermined value, the uniformity is low, that is, it is determined that there is no compatibility. .
  • the variance value is high, and it can be determined that there is no compatibility.
  • the shape feature is approximately represented as a frequency distribution for each direction of the local edge component.
  • the specific portion is, for example, near the center of the image data.
  • the method of determining the suitability based on the condition (3A) is as follows. First, as shown in Fig. 15 (a) and (b), pixels are run in the direction toward the center of the image data along a line segment drawn radially from the center of the image data at predetermined angles. ⁇
  • the difference between the luminance values of successive pixels is sequentially calculated in the scanning process, and the coordinate value ( X , y) of a point where the difference exceeds a predetermined value is stored.
  • step SB3 the feature data extraction unit 103 converts each feature data (color layout feature, texture feature, and shape feature) extracted in step SB2 into feature data. Stored in database 120 (see Figure 3).
  • step SB4 the feature data extraction unit 103 sets the image database 110 It is determined whether or not all the image data in (process of extracting and storing feature amount data) has been processed, and ⁇ , and the result of the determination is “No”. Thereafter, the above-described processing is performed on the remaining image data.
  • step SB4 When the process is completed, the result of the determination in step SB4 is set to “Y e s”, and the feature amount data storage process ends.
  • step SA4 a search process for searching for image data similar to the key image data is executed.
  • step SC1 shown in FIG. 6 the user inputs, for example, key image data 200 shown in FIG. 8A from the input unit 101.
  • the key image data 200 corresponds to the photographic images taken from the image data 1 1 1 3 different angles scenery in the same location as shown in Figure 8 (d).
  • step S C2 a suitability determination process is performed for the key image data 200 to determine suitability for each of the color layout features, the texture features, and the shape features.
  • the suitability determination unit 104 determines suitability of the color layout feature. That is, the suitability determining unit 104 divides the key image data 200 vertically and horizontally.
  • the key image data 200 is, as shown in FIG. 9, the vertical four equal parts, and those divided transversely to 4 equally divided 1 6 partial image data (I u ⁇ 1 44) I do.
  • the suitability determining unit 104 determines, for each of the partial images (I ti to I 44 ) of the key image data 200, each partial color space obtained by dividing R, G, and B in the RGB color space into four equal parts. The ratio value of the number of pixels included in the space is calculated, and the ratio value of the partial color space having the maximum ratio value is obtained as the value shown in FIG.
  • the ratio value ranges from [0.0, 1.0], and the larger the value, the larger the ratio.
  • Equation (1) for determining the degree of concentration SC in the method of determining the condition (2 A) of the color layout feature is 6, the portion having the ratio value shown in FIG. It is assumed that the calculation result of the degree of concentration s C of the image representative color is a value shown in FIG.
  • the degree of concentration SC is in the range of [0.0, 1.0], and the higher the value, the more concentrated.
  • the threshold value of the degree of concentration SC in the determination method of the condition (2A) of the color layout feature amount is 0.6, except for the partial image at the lower right shown in FIG. Satisfy condition (2 A). The partial image at the lower right does not satisfy the condition (2A).
  • step SD2 shown in FIG. 7 the suitability determining unit 104 determines the suitability of the texture feature. That is, as shown in FIG. 14 (a), the suitability determination unit 104 divides the key image data 200 into two equal parts vertically and horizontally and divides the test feature amount from each partial image into a vector / value. Extract.
  • the suitability determining unit 104 calculates the variance between the vector values as, for example, 0.7.
  • the variance value ranges from [0.0, 1.0], and it is assumed that the larger the value, the greater the variance.
  • the suitability determination unit 104 determines the suitability of the shape feature. That is, as shown in FIG. 15 (b), the suitability judging unit 104 divides the key image data 200 along a line segment radially drawn every 22.5 degrees from the center of the key image data 200. The pixels are scanned in the direction toward the center of the image.
  • the suitability determination unit 104 sequentially calculates the difference between the luminance values of successive pixels in the scanning process, and stores the coordinate value (x, y) of the pixel having a difference exceeding a predetermined threshold. I do.
  • the threshold value of the difference is 0.8 when the range of the difference value is [0.0, 1.0].
  • the result obtained by integrating the distance between the coordinate values between adjacent scanning lines is 1150 with respect to the coordinate values obtained from the results of performing the above for each scanning line segment.
  • the threshold value for this integrated value is 10000, the key image data 2000 has an integrated value larger than the threshold value, and it is determined that there is no suitability for the shape feature amount. You.
  • the key image data 200 is compatible with the color layout feature, but is incompatible with the texture feature and the shape feature.
  • the feature data extraction unit 1 ⁇ 3 extracts the feature data corresponding to the compatible type (in this case, the color layout feature) from the key image data 200. Is extracted.
  • the data is stored in the color layout feature data table 12 1 (see FIG. 3) of the feature database 120 in association with 0 0 0 0 0 4).
  • the compatibility determination unit 104 determines whether or not there is compatibility between the above type (color layout feature amount) and each image data to be searched stored in the image database 110.
  • the suitability determination unit 104 determines whether or not there is compatibility between the color layout feature amount and each image data in the same manner as in step SD1 shown in FIG. If the type is a texture feature, the suitability determination unit 104 determines whether or not there is compatibility between the texture feature and each image data in the same manner as in step SD2 shown in FIG. Is determined.
  • the suitability determination unit 104 determines whether or not there is compatibility between the shape feature and each image data in the same manner as in step SD3 shown in FIG. Is determined.
  • the similarity calculation unit 105 excludes the image data determined to be incompatible by the compatibility determination unit 104 from all the image data to be searched, and removes the image data determined to be compatible.
  • the similarity calculation unit 105 calculates the feature data (color layout feature) corresponding to the key image data 200 and the feature data corresponding to the key image data 1 1 1 1 1 1 2 and 1 1 1a, respectively. Calculate the critical distance from the data (color layout features). Rounding off to two decimal places, the result (Euclidean distance Is as follows.
  • the order of the similarity to the key image data 200 is as follows: image data 1 1 1 3 (see FIG. 8 (d)) in the first place, and image data 1 in the second place.
  • FIG. 8 (c) refers the third of the image data 1 1 ( Figure 8 (b)) as a reference.
  • step SC6 shown in FIG. 6 the search unit 106 obtains the search result from the image database 110 (see FIG. 2) in the order of the similarity obtained in step SC5.
  • Image data 1 1 1 3 , image data 1 1 1 2 and image data 1 1 1 i are obtained.
  • the search unit 106 causes the display unit 102 to display the search result screen 300 shown in FIG.
  • This search result screen 300 shows the search results when the color layout feature is used. From left to right in the figure, the key image data 200 and the image data 1 1 1 are arranged in descending order of similarity. 3 , image data 1 1 1 2 and image data 1
  • search results can be seen from the screen 3 0 0, for the key image data 2 0 0 (landscape photograph), image data 1 1 1 3 feel is more similar to humans (landscape photograph) is most similar
  • image data 1 1 1 2 and the image data 1 1 1 3 which are displayed at a position with a high degree and do not seem similar are displayed thereafter.
  • the texture feature data table 1 2 2 (3rd
  • the key image data 200 and the search target image data (image data 11 1 to 11 1) are used by using the feature amount data (texture space) stored in the The distance between 1 and 3 ) is as follows.
  • the order of the similarity with respect to the key image data 200 is as follows: the image data 1 1 1 is the first place (see FIG. 8 (b)), and the image data 1 1 1 2 is the second place the reference 8 view (c)), that Do a third of the image data 1 1 1 3 reference ( Figure 8 (d)).
  • the search unit 106 causes the display unit 102 to display the search result screen 310 shown in FIG.
  • This search result screen 310 shows the search results when the texture feature amount is used. From left to right in the figure, the key image data 200, the image data 1 1 1 Image data 1 1 1 2 and image data 1
  • the image data 111 which does not seem similar to humans, has the highest similarity to the key image data 200 (landscape photograph).
  • the image data 1 1 1 3 which is felt to be more similar is displayed at the position with the lowest similarity.
  • the shape feature data table 1 2 3 (see FIG. 3)
  • the Euclidean distance between the key image data 200 and the search target image data (image data 1 1 1 1 1 1 3 ) is calculated using the feature amount data (shape feature amount) stored in Become like
  • the search unit 106 causes the display unit 102 to display the search result screen 320 shown in FIG.
  • the search result screen 320 shows the search results when the shape feature is used. From left to right in the figure, the key image data 200, the image data 1 1 1 ⁇ Image data 1 1 1 3 and image data 1 1 1 2 are displayed.
  • the image data 111 which is not felt to be similar to humans, is located at a position with a high similarity to the key image data 200 (landscape photograph).
  • the image data 1 1 1 3 which are displayed and felt to be more similar are displayed in the middle position of the similarity.
  • the types (color layout feature, texture feature, shape feature) of each feature extracted from the key image data 200 correspond to human similarity. It is determined whether or not the matching similarity is calculated as the suitability. Based on the determination result, the similarity between the key image data 200 and each image data to be searched is calculated, and the similarity is calculated. Is output as search results (see FIGS. 16 to 18), so that the selection of the type of the feature amount applied when calculating the similarity is prevented, and Search accuracy can be improved. Also, according to one embodiment, in step S C5 shown in FIG.
  • 0 Determines the compatibility of the type of feature value determined to be compatible with 0 with each image data to be searched, and searches the image data determined to be unsuitable for each image data for the search target , And narrows down each image data determined to be relevant as search targets, and calculates the similarity between the key image 200 and each image data of the search target using the type feature amount. Therefore, the search accuracy and search efficiency can be further improved by narrowing down the search targets.
  • a program for realizing the functions of the image search device 100 shown in FIG. 1 is recorded on the computer-readable recording medium 500 shown in FIG. Then, the function of the image search device 100 may be realized by causing the computer 400 to read and execute the program recorded on the recording medium 500.
  • the computer 400 has a CPU (Central Processing Unit) 410 for executing the above program, an input device 420 such as a keyboard and a mouse, and a ROM (Read Only Memory) 430 for storing various data. , A RAM (Random Access Memory) 440 that stores calculation parameters, etc., a reading device 450 that reads a program from a recording medium 500, an output device 460 such as a display and a printer, and various parts of the device.
  • the connection path is made up of 470.
  • the CPU 410 reads the program recorded on the recording medium 500 via the reading device 450 and executes the program to realize the function of the image search device 100.
  • the recording medium 500 include an optical disk, a flexible disk, and a hard disk.
  • the similarity calculation unit 1 when there are a plurality of types (color layout features, texture features, etc.) determined to be compatible by the compatibility determination unit 104, the similarity calculation unit 1 In step 05, a result obtained by integrating similarities calculated for each type may be used as a similarity calculation result.
  • the result obtained by integrating the similarities calculated for each type is used as the similarity calculation result. Search accuracy can be improved from the viewpoint.
  • the compatibility determination section 104 when there are a plurality of types (color layout feature amount, texture feature amount, etc.) determined to be compatible by the compatibility determination section 104, one of the plurality of types is selected.
  • the user is allowed to select one type, and the similarity between the key image data 200 and each image data to be searched is calculated by the similarity calculation unit 105 using the feature amount of the type selected by the user. May be configured.
  • the user is allowed to approve the determination result of the suitability by the suitability determination unit 104, and the key image data 200 is determined based on the determination result that has been approved by the user.
  • the similarity calculation unit 105 may calculate the similarity with each image data to be searched.
  • the present invention for each type of feature extracted from a key image, it is determined whether or not a similarity matching a human similar sensation is calculated as the suitability. Based on the determination result, the similarity between the key image and each image to be searched is calculated, and the image corresponding to the similarity is output as the search result.
  • the selection effect of the type of the feature quantity to be prevented can be prevented, and if the search accuracy can be improved, the effect is obtained.
  • the present invention it is determined whether or not there is compatibility between the type of the feature amount determined to be suitable for the key image and each image to be searched, and it is determined that there is no compatibility for each image. Excluding images from the search target, narrowing down each image determined to be relevant as search targets, and calculating the similarity between the key image and each image of the search target by using the type feature amount. Therefore, by narrowing down the search targets, there is an effect that the search accuracy and the search efficiency can be further improved.
  • the present invention when there are a plurality of types determined to be compatible, the result obtained by integrating the similarities calculated for each type is used as the similarity calculation result. This brings about an effect that the search accuracy can be improved from a simple viewpoint. Further, according to the present invention, when there are a plurality of types determined to be compatible, the user is allowed to select one type from among the plurality of types, and using the characteristic amount of the selected type, Since the similarity between the key image and each of the images to be searched is calculated, it is possible to assist the user in selecting the optimal feature type. Can be improved Play a fruit.
  • the user is allowed to approve the determination result of suitability, and the similarity between the key image and each image to be searched is calculated based on the approved determination result.
  • the image search program according to the present invention is useful for image search using feature amount data of a plurality of types (for example, color layout feature amount, texture feature amount, shape feature amount).

Abstract

La présente invention a trait à un programme de recherche d'images comportant une unité d'extraction de données de quantité de caractéristiques (103) permettant l'extraction de données de quantité de caractéristiques d'une pluralité de types (tels que la quantité de caractéristiques de montage, la quantité de caractéristiques de texture, et la quantité de caractéristiques de forme) à partir de données de base d'images, une unité de détermination d'adaptation (104) permettant la détermination de la présence/de l'absence d'adaptation selon que le calcul de similitude corresponde ou non au sens de la similitude humaine pour les types de données de quantité de caractéristiques extraites par l'unité d'extraction de données de quantité de caractéristiques (103), une unité de calcul de similitude (105) permettant le calcul de similitude entre les données de base d'images et chaque donnée d'image à rechercher, selon le résultat de la détermination de l'unité de détermination d'adaptation (104), et une unité de recherche (106) permettant l'émission en sortie de données d'images correspondant à la similitude en tant que résultat de recherche.
PCT/JP2002/012135 2002-11-20 2002-11-20 Programme de recherche d'images WO2004047026A1 (fr)

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PCT/JP2002/012135 WO2004047026A1 (fr) 2002-11-20 2002-11-20 Programme de recherche d'images
JP2004553119A JP4160050B2 (ja) 2002-11-20 2002-11-20 画像検索プログラム
CNB02829713XA CN100346339C (zh) 2002-11-20 2002-11-20 图像检索方法及图像检索装置
US11/124,107 US20050210019A1 (en) 2002-11-20 2005-05-09 Method and apparatus for retrieving image from database, and computer product

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JP2010182295A (ja) * 2008-12-31 2010-08-19 Intel Corp グローバルな類似性に基づく分類法を用いた物体認識
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JP2012243197A (ja) * 2011-05-23 2012-12-10 Morpho Inc 画像識別装置、画像識別方法、画像識別プログラム及び記録媒体
JP2013196701A (ja) * 2012-03-16 2013-09-30 Fujitsu Ltd 画像処理装置、画像処理方法及び設備
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JP2007164648A (ja) * 2005-12-16 2007-06-28 Ricoh Co Ltd 類似画像検索装置、類似画像検索方法、プログラム及び情報記録媒体
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JP2011070244A (ja) * 2009-09-24 2011-04-07 Yahoo Japan Corp 画像検索装置、画像検索方法及びプログラム
JP2012243197A (ja) * 2011-05-23 2012-12-10 Morpho Inc 画像識別装置、画像識別方法、画像識別プログラム及び記録媒体
JP2013196701A (ja) * 2012-03-16 2013-09-30 Fujitsu Ltd 画像処理装置、画像処理方法及び設備
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