[go: up one dir, main page]

WO2002067143A1 - Analyse d"image pour indexage de base de donnees d"image - Google Patents

Analyse d"image pour indexage de base de donnees d"image Download PDF

Info

Publication number
WO2002067143A1
WO2002067143A1 PCT/GB2002/000675 GB0200675W WO02067143A1 WO 2002067143 A1 WO2002067143 A1 WO 2002067143A1 GB 0200675 W GB0200675 W GB 0200675W WO 02067143 A1 WO02067143 A1 WO 02067143A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
images
primitive
database
query
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/GB2002/000675
Other languages
English (en)
Inventor
Guoping Qiu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Nottingham
Original Assignee
University of Nottingham
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 University of Nottingham filed Critical University of Nottingham
Publication of WO2002067143A1 publication Critical patent/WO2002067143A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/5862—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using texture
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture

Definitions

  • This invention relates to image analysis, and in particular image processing and database management and to image analysis especially for the indexing, categorisation, and content-based retrieval of electronically stored images.
  • the invention further relates to image representation in general, and to image representation particularly for the matching, indexing, categorisation or retrieval of images based on the image content.
  • the problem therefore arises of developing an image analysis technique whereby the images held in a database may be analysed according to their content and the database may be instructed to supply all images which look similar to a target or query image.
  • US 5,852,823 claims a system, responsive to a query image set, for retrieving, from a stored database, a desired image that is visually similar to the image set, said set containing at least one query image, said system comprising: an image database having stored images therein; a signature generator, responsive to an input image and having three successive convolution filters, for producing a signature for the input image, the signature containing a numeric measure of each of a plurality of pre-defined characteristics of the input image; wherein the signature generator, in response to the query image set, generates a corresponding signature for each image in the query image set, and, in response to a test image accessed from the stored database, generates a corresponding test signature; a statistics generator, responsive to the signature for each image in the query image set, for providing a separate pre-defined statistical measure, for each of the pre-defined characteristics, that represents variability of said each pre-defined characteristic across all images in the query image set; a image database manager, operative in conjunction with the image database, for retrieving successive ones
  • a signature is computed for each image in a set using multi-level iterative convolution filtering, with pixel values supplied as input to each filtering level being separately convolved with each one of a set of predefined Gaussian kernels.
  • Average and variance vectors are separately computed across corresponding elements in all the image signatures for the set.
  • a linguistic term, semantically descriptive of all the images in the set is associated with the numeric descriptors of this set and, with this set, is stored in a database.
  • the descriptor for any set is accessed by a textual search through the database using the appropriate linguistic term.
  • the descriptor is then compared against accessed signatures for other images in the database in order to retrieve a image, among those stored in the database, that is the most similar to those in the set associated with the descriptor.
  • US 5,893,095 claims a search engine, comprising: a function container capable of storing primitive functions; a registration interface storing functions to the function container; and a primitive supplying primitive functions to the registration interface, wherein the primitive functions include an analysis function capable of extracting features from an object, together with a method of comparison using such a search engine, and it goes on to describe a system and method for content-based search and retrieval of visual objects.
  • a base visual information retrieval (NIR) engine utilises a set of universal primitives to operate on the visual objects.
  • NIR visual information retrieval
  • An extensible NIR engine allows custom, modular primitives to be defined and registered.
  • a custom primitive addresses domain specific problems and can utilize any image understanding technique.
  • Object attributes can be extracted over the entire image or over only a portion of the object.
  • a schema is defined as a specific collection of primitives.
  • a specific schema implies a specific set of visual features to be processed and a corresponding feature vector to be used for content-based similarity scoring.
  • a primitive registration interface registers custom primitives and facilitates storing of an analysis function and a comparison function to a schema table.
  • a heterogeneous comparison allows objects analysed by different schemas to be compared if at least one primitive is in common between the schemas.
  • a threshold-based comparison is utilised to improve performance of the NIR engine.
  • a distance between two feature vectors is computed in any of the comparison processes so as to generate a similar
  • the present invention provides an image analysis system, which may be a processing or representation system, in which an input image is deconstructed into primitive images corresponding to palette features of the input image and in which the geometric properties of the binary images are measured and populations of different geometrical measures of the respective palette features are tabulated.
  • an image analysis system which may be a processing or representation system, in which an input image is deconstructed into primitive images corresponding to palette features of the input image and in which the geometric properties of the binary images are measured and populations of different geometrical measures of the respective palette features are tabulated.
  • the present invention also provides an image indexing system in which the image is deconstructed into a plurality of primitive images each reflecting a palette feature of the original image and then tabulating the populations of distinct geometrical features of each such primitive image.
  • the invention includes an image retrieval system for retrieving images similar to a query image from an image database in which an algorithm is applied to the query image to deconstruct the query image into a plurality of primitive query images each reflecting a palette feature of the original query image and then tabulate the populations of distinct geometrical features of each such primitive query image to form a query table, and then comparing the query table with image tables created by applying said algorithm to images held in said database.
  • the invention includes an image database which contains image tables created by applying an algorithm to each of a set of images to deconstruct each of those images into a plurality of primitive images each reflecting a palette feature of the original image and then tabulating the populations of distinct geometrical features of each such primitive image to form a lookup table.
  • the invention also includes a workstation programmed with an algorithm adapted to be applied to an image to deconstruct that image into a plurality of primitive images each reflecting a palette feature of the original image and then to tabulate the populations of distinct geometrical features of each such primitive image to form a digital table.
  • Such workstation may be remote from or wired to a database storing images optionally operated on by said algorithm.
  • the workstation is suitably programmed to apply the algorithm to a query image to form a query table and then to compare the query table with image tables created by applying said algorithm to images held in said database, in order to retrieve images similar to the query image.
  • the invention extends to a method of retrieving images retrieving images similar to a query image from an image database in which an algorithm is applied to the query image to deconstruct the query image into a plurality of primitive query images each reflecting a palette feature of the original query image and then tabulate the populations of distinct geometrical features of each such primitive query image to form a query table, and then comparing the query table with image tables created by applying said algorithm to images held in said database.
  • the invention also extends to a method of indexing images in which an algorithm is applied to the image to deconstruct it into a plurality of primitive images each reflecting a palette feature of the original image and then to tabulate the populations of distinct geometrical features of each such primitive image.
  • the invention further extends to an object recognition system in which an image is formed of the object to be recognised, an algorithm is applied to the object image to deconstruct the object image into a plurality of primitive object images each reflecting a palette feature of the original object image and then tabulate the populations of distinct geometrical features of each such primitive object image to form a query table, and then comparing that query table with image tables created by applying said algorithm to images held in an image database.
  • the invention includes a method of creating an image content table which comprises: defining palette features of an image and deconstructing the image into a plurality of binary primitive images each reflecting a said palette feature;
  • the invention also includes an image content table which comprises a tabulated list of populations of different geometrical features of each of a plurality of primitive binary images .
  • the primitive images are binary images having a plurality of pixels each having one of two values associated with it.
  • each pixel in the binary image is associated with a respective part of the deconstructed image from which it is formed.
  • Each said part of the deconstructed image most conveniently comprises a pixel of the deconstructed image.
  • the primitive images may each indicate which of said parts have the respective palette feature and which do not.
  • each palette feature corresponds to a respective one of the classifications .
  • each palette feature has a palette feature vector associated with it and each pixel of the image is classified by defining a pixel feature vector for the pixel and determining which of the palette feature vectors it is closest to.
  • the at least one image feature may include surface texture, or colour.
  • each image feature has a plurality of possible categories, and each defined palette feature corresponds to a plurality of said categories.
  • geometric features of each primitive image are recorded in an array, for example a one-dimensional array, and the arrays for the primitive images are combined to form a table.
  • the geometric properties of the primitive images include geometric properties of connected elements within the primitive images.
  • the geometrical features tabulated may be of various kinds. Generally, the tabulation is based on an analysis of the rows and columns of pixels making up the primitive binary image, though other analytical systems are possible, for example by referring to blocks of pixels, for example connected blocks of pixels .
  • the tabulation comprises a table of the populations of rows and columns of pixels of each palette feature of a given length or height.
  • the table may reflect the presence of Rj rows made up of one pixel, R 2 rows made up of two pixels and so on, and of Ci columns one pixel high, C 2 columns which are two pixels high, and son on.
  • Rj rows made up of one pixel
  • R 2 rows made up of two pixels and so on
  • Ci columns one pixel high
  • C 2 columns which are two pixels high, and son on.
  • the geometrical features may thus be considered as rows and columns of a given pixel length or height.
  • the tabulation comprises the total number of pixels containing the selected palette feature in each row and column.
  • the geometrical feature is thus simply the active pixel content of each row or column.
  • the geometrical feature maps to the total size of each continuous array of pixels of a given palette feature.
  • the palette feature analysed may be a colour tone, a pattern or texture, or any other tonal value contributing to the appearance of the image which is being subjected to the algorithm.
  • at least one said palette feature represents a collocation of a plurality of visually distinguishable tonal values of the respective image.
  • the algorithm may be adapted to reduce the colour palette prior to tabulation. For example the image may be reduced to a colour palette of 128 colours, or of 64 or 32 or even fewer colours. This of course greatly reduces the memory space required for storing the tabular information required if it is desired to maintain an entire database in tabular form.
  • the algorithm may be applied in such a way that, for example, all blue tones are combined to a single blue palette feature and the image is tabulated on that basis.
  • the invention is not of course confined to the amalgamation of like colours into a single palette feature. But the algorithm applied to the stored images and the query image must be compatible.
  • the present invention takes the previously known techniques a step further by providing a method of representing image contents based on the co-occurrence of geometric features and surface appearance features and summarising the co-occurrence statistics in a tabular form.
  • a number of innovative features of the current invention make this invention advantageous over prior art technologies.
  • the use of the co-occurrence of the surface appearance features (such as colour tone, texture characteristics etc) and the geometric structure (shape, size, and other parameters) of image areas of homogeneous appearance makes the representation more discriminative, thus enabling more accurate image retrieval.
  • the decomposition of the image into binary primitive form also allows fast and flexible processing.
  • the present invention is not only applicable to image indexing and retrieval but also has more general application in general image matching and object recognition.
  • Figure 1 is a general schematic diagram illustrating image content table generation
  • Figure 2 illustrates a palette feature table relating to different colours
  • Figure 3 illustrates a palette feature table relating to different texture features
  • Figure 4 illustrates a classifier
  • Figure 5 shows a binary image used to illustrate the construction of horizontal and vertical pixel arrays
  • Figure 6 shows an 8-connected pixel array and a 4 connected pixel array
  • FIG. 7 illustrates connected component labelling
  • Figure 8 illustrates how a primitive binary image may be divided into horizontal and vertical patches
  • Figure 9 is an example illustrating the calculation of vertical and horizontal projections
  • Figure 10 is an image content table (ICT) based on Horizontal and Vertical Line Measurement
  • Figure 11 is an image content table (ICT) based on Connected Component Measurement
  • Figure 12 is an image content table (ICT) based on Horizontal and Vertical Projections;
  • Figure 13 illustrates an application of Image Content Table to Image Database Indexing and Retrieval;
  • Figure 14 illustrates the construction of object class prototypes/models and a recognition model
  • Figure 15 illustrates steps involved in the recognition of an unknown object.
  • the image analysis method of the invention uses a novel concept of image content table (ICT) .
  • Fig. 1 shows the schematic of a method for generating an ICT.
  • the input image is first passed through a classifier, which classifies the input image pixels into a number of classes (N) according to the palette features (PFs) which are to be analysed.
  • N the number of classes
  • PFs palette features
  • These palette features result in N feature vectors characterising image tonal value properties such as colour tones or surface pattern or texture properties. It is not essential that each individual visually or otherwise distinguishable colour or other tonal value should be assigned to a palette feature giving rise to a feature vector. Indeed, it is preferred that the various colour tones of the input image should be assigned to a reduced colour palette in which a single palette feature represents a plurality of tonal values of the input image. Thus the various colour tones of the input image are preferably assigned to one of, for example, 128 different palette features. In a simpler system requiring even less memory, the palette used for analysing the input image contains 64 or 32 or 16 or 8 colours or features.
  • N primitive binary images are formed, each of the same dimensions as the input image.
  • Each PBI corresponds to a palette feature (vector), and the pixel values of the PBI indicate the presence or absence of the palette feature in the input image at the pixel locations.
  • a pixel value of 1 in the PBI indicates the presence of the feature and a pixel value of 0 indicates the absence of that feature. (However, since it is a binary image, assigning the pixel values the other way round is also possible) .
  • the primitive binary images are then analysed by the binary image analyser, whose primary function is to characterise the geometric features of the 1 -valued pixels in the PBI's.
  • a two-dimensional table, the image content table (ICT) which characterises the frequencies of the co-occurrence of the palette features and the geometric features is then generated.
  • Palette Feature Vectors The palette features are contained in a pre-determined table consisting of N entries, each pointing to an image feature vector.
  • image features There are various properties of the input image which may be considered as image features : these include (1) colour, and (2) surface texture properties. Other features are possible.
  • the palette feature table for the case where colour tones are used as image features is shown in Fig. 2.
  • each palette feature is characterised by a 3-dimensional vector in an appropriate colour space, and for illustration purposes, an RGB colour space is used here. It is to be noted that the use of other colour spaces is also possible.
  • palette features that characterise the surface texture or pattern or other properties of the image.
  • the palette table for the case when texture properties are used as image features is shown in Fig.3, where it is assumed the texture property of a pixel location is characterised by an m-dimensional texture feature vector, obtained for example by a filtering based approach.
  • the texture features can be used to describe the texture surface of a grey-scale as well as colour images (see A. Jain and G. Healey, "A multiscale representation including opponent color features for texture recognition" , IEEE Transactions on Image Processing, vol. 7, no. 1, pp. 124 - 128, January 1998) .
  • the N feature vectors should be chosen as statistically representative vectors in the chosen feature space.
  • vector quantization [Reference 4] and use as many example images as possible to generate training samples.
  • the process of obtaining the palette feature vectors needs only be performed once, and after feature vectors are generated they are fixed and stored for used by the classifier. (Generating the palette feature table can be easily done by those known in the art e.g. Gerrastz, M. & Purgathofer, W.
  • X denote the PFV
  • X (r x , g x , b x )
  • this PFV is the m-dimensional texture characteristic feature vector.
  • X (T.(l), T x (2) , ...,T x (m)) .
  • Each pixel's PFV in the original input image is compared with the N palette feature vectors in the Euclidean space.
  • the classifier outputs the index of the palette feature vector that is the closest to the PFV, X.
  • each pixel in the input image will have been assigned an index, the pixel feature index (PFI), ranging from 1 to N, indicating the assignment of the pixel into one of the N palette features.
  • PFI pixel feature index
  • a pixel in the i-th primitive binary image will have a value of 1 if the pixel in the corresponding location in the input original image is associated with the i-th palette feature in the palette feature table, and a value of 0 if the pixel in the corresponding location in the input original image is assigned to any other palette feature.
  • a set of line length values, Li, L 2 , ...L ⁇ is pre-set.
  • G V (K) ⁇ whose elements are the number of connected vertical lines of lengths L ⁇ L 2 , ...L ⁇ . Note that the number of line length values and indeed the values of the line length for horizontal and vertical direction do not have to be the same.
  • connected 1-valued pixels are given the same label.
  • Two pixels can be 8-connected or 4-connected as illustrated in Figs. 6a and 6b respectively.
  • Fig. 6a pixel 1 and pixels 2, 3, 4, 5, 6, 7, 8, and 9 are 8-connected
  • Fig. 6b pixel 1 and pixels 2, 3, 4, 5 are 4- connected.
  • 8-connected pixels are considered here.
  • Another method of analysing the primitive binary images is based on the projection of the 1-valued pixels along horizontal and vertical directions. Assuming the original input image consists of M (row) x N (column) pixels, we divide the binary image into horizontal and vertical patches, each consists of equal number of rows or columns of pixels, as illustrated in Fig. 8. Assuming we are dividing the image into V (1 ⁇ V ⁇ N) vertical and H (1 ⁇ H ⁇ M) horizontal patches, then each vertical patch will consists of INT(N/V) columns of pixels, and each horizontal patch will consist of INT(M/H) rows of pixels. Notice INT(x) denotes the integer part of x.
  • Fig. 9 shows an example calculating the vertical and horizontal projection.
  • the original image is 12 rows x 13 columns (pixel) .
  • We want to divide it into 6 horizontal patches and 7 vertical patches. Since 13/7 1.86 is not an integer, we duplicate the last column of pixels to make the image 12 rows x 14 columns (pixel) . Therefore, each vertical patch consists of two columns of pixels, and each horizontal patch consists of 2 rows of pixels.
  • an image content table (ICT) is generated.
  • the ICT is a two dimensional array formed by arranging the 1 -dimensional geometric measurement arrays produced by the image analyser.
  • the table will be different and the construction of the ICT for each of the three measurements described above is illustrated in Figures 10 to 12 respectively.
  • the image content table is formed by stacking GHL(i) and GVL(i) to form the i-th column of the 2-dimensional ICT as shown in Fig. 10. ICT based on Connected Component Measurement (Fig. 11)
  • G s (i) ⁇ G s (i, 1), G s (i, 2) , ..., G s (i, K) ⁇ be the geometric property measurement array generated by the image analyser based on the method of Connected Component Measurement for the i-th primitive binary image.
  • the image content table, ICT is formed by arranging G s (i) into the i-th column of the ICT as shown in Fig. 11.
  • the image content table is formed by stacking GPV (i) and GPH (i) to form the i-th column of the 2- dimensional ICT as shown in Fig. 12.
  • Image Content Table for Image Database Applications A principal application of the image content table is in image database management. It can be used in content-based image indexing and retrieval. The scenario can be illustrated in Fig. 13.
  • an image content table (ICT) is constructed and stored on the disk as part of the image database.
  • Such a database constructed using our ICT can be used to implement a content-based image query, or a query by pictorial example.
  • a user has an image and wants to find from the database images that are similar to the query image.
  • An image content table, ICTQ is first constructed for the query image. Then this ICTQ is compared to the ICTs of the database.
  • a similarity scorer calculates a metric measurement between the query image's ICT and the ICTs of the database images.
  • One possible implementation of the Similarity Scorer is as follows.
  • the similarity score between the two images can be calculated:
  • each image in the database can be given a similarity score.
  • All the scores may be sorted in an increasing order (the smaller the score, the more similar the images) and returned to the user. From this sorted list, the user can identify the images from the database that are most similar to the query.
  • the image content table has more general applications in machine vision based automatic object recognition.
  • an object is first imaged using a sensor, e.g. , a CCD camera.
  • the image of the object is deconstructed; e.g. , using a pre-designed colour palette table, into a set of primitive binary images and an image content table is constructed to represent the image (object) .
  • a sensor e.g. , a CCD camera.
  • the image of the object is deconstructed; e.g. , using a pre-designed colour palette table, into a set of primitive binary images and an image content table is constructed to represent the image (object) .
  • general automatic object recognition machine systems can be constructed.
  • Fig. 14 illustrates how the ICT can be used in constructing object class prototype/model and recognition models.
  • Each sample object of known class identity is imaged and represented using an ICT, which can be regarded as a feature vector (pattern) .
  • pattern can be used to construct object class prototypes/models using an appropriate recognition strategy.
  • Fig. 15 illustrates the use of the object class prototype/model ICT to recognise unknown objects.
  • An unknown object is first imaged and then the image of the unknown object is represented in image content table form and presented to the same pattern recognition algorithms which will use the object prototype/model information to identify the class identity of the unknown object automatically.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Library & Information Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Geometry (AREA)
  • Probability & Statistics with Applications (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Cette invention concerne un procédé d"analyse d"images pour la classification, l"indexage et la recherche d"images, avec formation d"une pluralité d"images binaires primitives représentant chacune la présence ou l"absence d"une caractéristique de palette pour chaque pixel de l"image, analyse des propriétés géométriques de chacune des images binaires primitives dans le but de former un ensemble unidimensionnel, et combinaison des ensembles en vue de la création d"une table de contenu pour l"image considérée.
PCT/GB2002/000675 2001-02-17 2002-02-15 Analyse d"image pour indexage de base de donnees d"image Ceased WO2002067143A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GBGB0103965.0A GB0103965D0 (en) 2001-02-17 2001-02-17 Image and image content processing,representation and analysis for image matching,indexing or retrieval and database management
GB0103965.0 2001-02-17

Publications (1)

Publication Number Publication Date
WO2002067143A1 true WO2002067143A1 (fr) 2002-08-29

Family

ID=9908976

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2002/000675 Ceased WO2002067143A1 (fr) 2001-02-17 2002-02-15 Analyse d"image pour indexage de base de donnees d"image

Country Status (2)

Country Link
GB (1) GB0103965D0 (fr)
WO (1) WO2002067143A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012033460A1 (fr) * 2010-09-10 2012-03-15 Choros Cognition Ab Procédé pour classer automatiquement une image à deux dimensions ou plus
CN103164436A (zh) * 2011-12-13 2013-06-19 阿里巴巴集团控股有限公司 一种图像搜索方法及装置
CN115457167A (zh) * 2022-09-21 2022-12-09 山东大学 基于色彩排序的调色板设计系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0866409A1 (fr) * 1997-03-19 1998-09-23 Canon Kabushiki Kaisha Appareil et méthode de recouvrement d'images
EP1045313A2 (fr) * 1999-04-14 2000-10-18 Eastman Kodak Company Archivage et recouvrement d'images basés sur des caractéristiques perceptiblement importantes
GB2349460A (en) * 1999-04-29 2000-11-01 Mitsubishi Electric Inf Tech Representing and searching for colour images

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0866409A1 (fr) * 1997-03-19 1998-09-23 Canon Kabushiki Kaisha Appareil et méthode de recouvrement d'images
EP1045313A2 (fr) * 1999-04-14 2000-10-18 Eastman Kodak Company Archivage et recouvrement d'images basés sur des caractéristiques perceptiblement importantes
GB2349460A (en) * 1999-04-29 2000-11-01 Mitsubishi Electric Inf Tech Representing and searching for colour images

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
GONG Y: "ADVANCING CONTENT-BASED IMAGE RETRIEVAL BY EXPLOITING IMAGE COLOR AND REGION FEATURES", MULTIMEDIA SYSTEMS, ASSOCIATION FOR COMPUTING MACHINERY, NEW YORK, US, vol. 7, no. 6, pages 449 - 457, XP000912566, ISSN: 0942-4962 *
KANKANHALLI M S ET AL: "Color and spatial feature for content-based image retrieval", PATTERN RECOGNITION LETTERS, NORTH-HOLLAND PUBL. AMSTERDAM, NL, vol. 20, no. 1, January 1999 (1999-01-01), pages 109 - 118, XP004154223, ISSN: 0167-8655 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012033460A1 (fr) * 2010-09-10 2012-03-15 Choros Cognition Ab Procédé pour classer automatiquement une image à deux dimensions ou plus
US9036924B2 (en) 2010-09-10 2015-05-19 Choros Cognition Ab Method for automatically classifying a two-or higher-dimensional image
CN103164436A (zh) * 2011-12-13 2013-06-19 阿里巴巴集团控股有限公司 一种图像搜索方法及装置
CN103164436B (zh) * 2011-12-13 2017-06-16 阿里巴巴集团控股有限公司 一种图像搜索方法及装置
CN115457167A (zh) * 2022-09-21 2022-12-09 山东大学 基于色彩排序的调色板设计系统

Also Published As

Publication number Publication date
GB0103965D0 (en) 2001-04-04

Similar Documents

Publication Publication Date Title
Chen et al. A region-based fuzzy feature matching approach to content-based image retrieval
Schettini et al. A survey of methods for colour image indexing and retrieval in image databases
US6584221B1 (en) Method for image retrieval with multiple regions of interest
Fournier et al. Retin: A content-based image indexing and retrieval system
Smeulders et al. Content-based image retrieval at the end of the early years
Liu et al. A survey of content-based image retrieval with high-level semantics
Iqbal et al. Applying perceptual grouping to content-based image retrieval: Building images
CN100357944C (zh) 图像检索系统和方法及图像分类系统和方法
US8150216B2 (en) Methods and apparatus for automated true object-based image analysis and retrieval
US20030147558A1 (en) Method for image region classification using unsupervised and supervised learning
US7545980B2 (en) Method of and apparatus for classifying an image
Moghaddam et al. Defining image content with multiple regions-of-interest
CN103377376A (zh) 图像分类的方法和系统、图像检索的方法和系统
Shih et al. An intelligent content-based image retrieval system based on color, shape and spatial relations
Chiang et al. Region-based image retrieval using color-size features of watershed regions
Khotanzad et al. Color image retrieval using multispectral random field texture model and color content features
Jaswal et al. Content based image retrieval using color space approaches
White et al. ImageGREP: Fast visual pattern matching in image databases
Zhang et al. Modelling traditional Chinese paintings for content-based image classification and retrieval
WO2002067143A1 (fr) Analyse d"image pour indexage de base de donnees d"image
Prakash et al. Combining novel features for content based image retrieval
Goswami et al. RISE: a robust image search engine
Kurniasari et al. Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features
Aparna Retrieval of digital images based on multi-feature similarity using genetic algorithm
Elhady et al. Weighted feature voting technique for content-based image retrieval

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ OM PH PL PT RO RU SD SE SG SI SK SL TJ TM TN TR TT TZ UA UG US UZ VN YU ZA ZM ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP

WWW Wipo information: withdrawn in national office

Country of ref document: JP