EP2050022A1

EP2050022A1 - Method for producing scaleable image matrices

Info

Publication number: EP2050022A1
Application number: EP07786557A
Authority: EP
Inventors: Maximilian Schich
Original assignee: Max Planck Gesellschaft zur Foerderung der Wissenschaften eV
Current assignee: Max Planck Gesellschaft zur Foerderung der Wissenschaften eV
Priority date: 2006-08-07
Filing date: 2007-08-03
Publication date: 2009-04-22
Also published as: US20100194756A1; WO2008017430A1

Abstract

A method for producing an image matrix (1) comprises the steps of providing a network having a quantity of link starting nodes (2) and a quantity of link destination nodes (3) with intervening links (4), forming a matrix (5) having rows and columns, wherein the link starting nodes are assigned to the rows and the link destination nodes are assigned to the columns, or vice versa, and positioning visual representations (6) of the link starting nodes (2) or of the link destination nodes (3) in place of the links in the matrix, thus resulting in the image matrix (1).

Description

Method of producing scalable image matrices

Subject of the invention

The present invention relates to a method for producing image matrices. The method belongs to the technical fields of image science, data processing and network science (so-called "Science of Complex Networks").

State of the art

An image matrix is generally a two-dimensional array of images in rows and columns. The position of the image (row, column) in the image matrix represents information about the relationship of the image to the contents (meanings) of the associated row and column positions and a relationship between the row and column positions. A

Image matrix is a visualization (display) of the images, which has previously enabled the viewer to recognize relationships between images or between row and column positions.

Picture matrices exist at least since 1181, the Klosterneuburg altar ^Λ of Nicholas of Verdun, which visualizes the network of typological references in the Bible. A modern example is the architectural project, Schedule of the Las Vegas Strip hoteis' (see http: // www library unlv edu / arch / las / map / index2 html or in the book, Learning from Las Vegas ^Λ Venturi.... Scott-Brown and Izenour (London / Cambridge 1977)), in which each one of the image matrix a hotel in Las Vegas, z. B. Hotel "Paris", and one column each of the image matrix of a feature of hotels, eg. B. is associated with the appearance of the facade. The consideration of the image matrix allows z. B. a comparison of the features of the hotels.

All known examples, however, have in common that the image matrix is generated by the images contained (images) are specially created in the production of image matrices. An initially empty table is filled with new images. A conventional image matrix merely provides a systematic presentation of pre-existing ones

Information without allowing further evaluation of the information. Conventional image matrices typically present only positive correlation, i. the existence of a relationship, between features, but not negative correlation, i. the absence of such a relationship, between features.

It will be appreciated from the prior art that the examination of larger quantities of classified items is usually tied to individual representations (such as data sheets of a single item) or to one-dimensional list representations (such as result lists or one-dimensional overview tables).

Phenomena in which there is a connection between the

Classification and the visual properties of the objects or the classification criteria, therefore, are difficult to study, especially with the conventional image matrices.

Object of the invention

It is therefore an object of the invention to provide an improved method which has the disadvantages mentioned above eliminates. A further object of the invention is to provide a storage medium or an electronic data processing system comprising a processor and a storage medium in order to carry out the method.

This object is achieved by a method having the features of claim 1. Advantageous embodiments and applications of the invention will become apparent from the dependent claims.

Summary of the invention

The object is achieved by a method having the features listed in claim 1: providing a network with a set of link output nodes and a set of link destination nodes with links therebetween is followed by forming a matrix with rows and columns, with link output nodes associated with the rows and link target nodes to columns or vice versa. Finally, the searched image matrix is generated by placing visual representations of the left-hander nodes or link-destination nodes in place of the links in the matrix.

In contrast to existing image matrices, which result from the filling of an empty table with custom images, existing image information of nodes of a (classification) network replaces the links in the cells of the matrix in the presented method. The invention provides an image matrix which comprises a visualization (display) of images and advantageously enables the viewer to recognize or produce relationships between images and / or to subject the images to data processing and / or data maintenance. The advantage achieved by the invention is, in particular, that the invention, when dealing with larger quantities of classified objects, facilitates the investigation of phenomena in which there is a connection between the classification and the visual properties of the objects and / or the classification criteria.

Among other things, the method facilitates the explication of direct dependencies as well as the extraction of diachronic phenomena from a given set of classified (BiId) data. Furthermore, in contrast to conventional list representations and overview tables, the method allows the simultaneous examination of data in the context of two data dimensions. Compared to the prior art, this means a considerable acceleration of the work, since cumbersome navigation in the amount of data is eliminated.

The method advantageously represents a completely or partially automatable tool for processing large amounts of data. The image matrix according to the invention can be constructed from the data volume without prior knowledge, in particular without the user's knowledge of existing correlations between data.

According to an advantageous embodiment of the method, link output nodes of the above-mentioned network are classifiable objects and link destination nodes classification criteria or vice versa.

According to a further advantageous embodiment, objects and / or classification criteria thereby comprise a set of persons, locations, time ranges, physical objects, conceptual objects, events and periods. An event is the meeting of several of the aforementioned objects in a node, ie, for example the gathering of a physical object, a location, and a time range in a residence event. Periods include continuous, non-discrete expansions in one or more of the named subject dimensions, ie, for example, a style period that has both spatial and temporal extent over multiple locations and time ranges.

An advantageous embodiment of the specified features is given if objects and / or classification criteria are represented by individual nodes or as a group of nodes. Items and classification criteria represented by a group of nodes are multi-part. Optionally, there are multi-valued links between multipart objects and classification criteria, since more than one node of the respective multipart article or classification criterion can be linked. In this case, multipart objects are possibly linked only indirectly, eg via subordinate subnodes with a classification criterion. As the relationship between source node and destination node expressed in the value of the matrix cell thus becomes considerably more complex, it is referred to below as "edge ^Λ " for better distinction. The edge between a (multi-part) object and a (multi-part) classification criterion can contain one link or several links or be empty.

In a further advantageous embodiment of the method, it is provided that in the case of a multi-valued link of / or to a multi-part article or a multi-part classification criterion, either a detail image matrix, a one-dimensional overview table or an image montage is placed. In a further advantageous embodiment of the method, it is provided that multipart, in particular hierarchically subdivided objects or classification criteria in the matrix are unfolded in an input signal into a plurality of matrix rows or matrix columns or combined into a matrix row or matrix column.

In a further advantageous embodiment of the method, it is provided that individual objects and / or classification criteria can be connected in the image matrix on the basis of more precisely specifiable relationships.

Advantageously, the image matrix can be output in the course of the method on an output device, in particular on a screen or a printer, or in a file.

According to a further advantageous embodiment of the invention it can be provided that additional information is additionally placed on the matrix elements of the image matrix, which are retrieved from a database and belong to the respective link output nodes or link destination nodes. Such additional information may include, for example, further data relating to a visualized image.

According to a further advantageous embodiment of the invention, a data processing can be provided, in which the data of the image matrix (images, texts and / or other information to the matrix elements) further processing, preferably a data input, an image recognition, a correlation and / or a rearrangement of the data. The edited data is then stored and / or output as a processed (modified) image matrix. According to a further advantageous embodiment of the invention, the storage of the processed data can be done in an amount of data from which the provision of the network takes place. Advantageously, thus the information of the data volume can be automatically enriched and completed for further use.

The cited developments of the invention advantageously allow:

a) the easier handling of the ambivalence of the higher unity of objects and classification criteria (advantage 1),

b) a simpler approach to justification of the correlation of objects (advantage 2),

c) the extraction of time-bound phenomena with regard to objects such as the classification criteria (advantage 3),

d) an easier answer to implicit visual questions (advantage 4), as well as

e) a facilitation of data analysis and revision of the output data set (advantage 5).

The detailed explanation of the above advantages 1 to 5 follows the explanation of the invention at the end of the description.

The method can be used, for example, in the areas of bibliometrics (explication of implicit picture quotations), art history (reception, tradition, mnemosyne), network science and copyright issues. Further objects of the invention are a storage medium and / or an electronic data processing system, which comprise a processor and a storage medium, wherein the storage medium includes software that causes the processor to carry out the inventive.

Brief description of the drawings

Further advantages and details of the invention are illustrated in the drawings and will be described in more detail below with reference to preferred embodiments of the invention. Show it:

FIG. 1 shows a flow chart with an illustration of the method according to the invention;

Figure 2a-c matrices with increasing information content;

Figure 3 illustrates the formation of the image matrix, with visual representations of the nodes replacing the links;

FIGS. 4a-c image matrices with increasing information density;

FIG. 5 Within an image matrix, the assembly of relevant sub-nodes leads to better comparability of the representations;

FIG. 6 shows three simple steps from the matrix to the image matrix; FIG. 7 is a block diagram of the general procedure for producing an image matrix;

FIG. 8 shows the raw form of the base list (adjacency list);

FIG. 9 shows the extraction of various record numbers (node IDs) in the base list, which permits the external answering of simultaneous local, global and metalocal questions or the reconstruction of a tree structure;

FIG. 10 shows a general procedure for producing a matrix (detail from FIG. 7);

FIG. 11 shows a detail of a matrix;

FIG. 12 shows a section of an image matrix;

FIG. 13 shows an illustration of an edge, which may contain a different number of links in matrix rows or matrix columns of different abstract;

FIG. 14 shows a detail image matrix which offers a better allocation of information, while a detailed overview offers larger images on a comparable surface;

FIG. 15 shows a scaling or zooming of a matrix: local> metalocal> global; and

FIG. 16 shows strict node trees which are similar in zooming to the directory tree in a conventional operating system. Allow system (icons to Windows Explorer ™).

Preferred embodiments of the invention

There follows the explanation of the invention with reference to the drawings according to the structure and operation of the illustrated invention. Embodiments of the invention are explained below with reference to the art of painting and the arts. The implementation of the invention is not limited to this application, but possible in other areas, which is exemplified below. Some of the figures illustrate in particular for the construction of a matrix or image matrix individual text features, such. For example, table values which, for reasons of printing technology, are only reproduced in reduced form and z. T. can be understood as illustrative images.

The main steps of a preferred embodiment of the inventive method are shown in Figure 1 and in the block diagram of Figure 7. First, at step Sl, the provision of data in at least one database ('data set' in Figure 7). The data includes link output node data, link destination node data, and data characterizing the links between the link outbound node and the link destination node. The data can generally be present as image and / or text data, wherein the data can be visualized by at least one of the link output nodes and the link destination nodes (eg also set text of a scanned book page). The data is provided at step S2 in the form of a base list (BASE in Figure 7, contents eg Figure 8) which contains all the information necessary to construct the matrix and the image matrix. The basic list is a data list with the structure described below, which in one Data store is stored, which may be connected to or extracted from the database.

On the basis of the information contained in the base list, a matrix is constructed at step S3 whose

Row and column positions are formed by the listing of link outbound nodes and link destination nodes (or vice versa). The matrix elements (ie, the cells of the matrix) comprise a zero (no information) if there is no link between the link output nodes and link destination nodes of the associated rows and columns, or a matrix element containing information about a single or multi-valued link between the associated link output nodes and Link destination node includes. This information is obtained from the so-called "edge-set" information from the base list, where a function (subroutine) is placed at the respective matrix elements, with which it is queried whether the relevant combination of row and column occurs in the "edgeset". If so, the valence of the relationship (valence of the link) is queried. A univalent link will do that

Image of the associated link output node or link destination node to the location of the matrix element set (Figure 3). In the case of multi-valued links, a detail matrix (FIG. 4a), an overview table (FIG. 4b) or an image montage (FIG. 4c) are placed in place of the matrix element.

Finally, in step S4, the desired image matrix is constructed from the matrix, in which matrix elements are replaced by visual representations of the associated link output nodes or link destination nodes. In this case, a selection of the visual representation depending on the valency of the link (edge value) can take place. With the provision of the image matrix, as a rule, a finished process result is available. The image matrix comprises the data of images associated with rows and columns of the image matrix, and possibly additional information. The data is available to a user who, for. B. wants to investigate relationships between images or between row and column positions.

The further use of the image matrix is simplified if at least a part (detail) of the image matrix is output. An output of the image matrix may be made to a display device (e.g., display, printout) or to a data memory (step S5). To output the image matrix, a decryption of the image matrix takes place.

Optionally, a further data processing can be provided after step S3, S4 and / or S5, in which the images, texts and / or further information of the image matrix are subjected to further processing (step S6). It can z. For example, further information may be input from other data resources to further enrich the information represented by the image matrix. An image recognition may be provided to detect and evaluate certain images (patterns) in the cells of the matrix. Between the images, if necessary after the image recognition, a correlation of specific partial images may be provided in order to establish relationships. Furthermore, a rearrangement of the data can be provided. The data processing in step S6 may be performed by a user or automatically by available data processing programs, which for the respective functions, eg. B. image recognition, correlation are established. In the case of the jump from step S3 directly to the data processing (step S6), the image matrix is created after a renewed passage from Sl to S3 to S6 (step S4). The edited data is then saved. The storage can be done in the output dataset or in a separate memory. Alternatively or additionally, a modified image matrix can be constructed with the processed data.

The details in particular of the steps S1 to S4 are explained below. The practical implementation is carried out using known methods and software tools, such as a spreadsheet or HTML, the details of which are not described here. Alternatively, the method could e.g. also as part of an application in the so-called 'Semantic Web' with the help of JAVA and AJAX or the like. be implemented.

1. Introduction

A lot of classified items can be e.g. understand as a network of nodes and links. Objects and classification criteria each form a node type; the assignment of an item to a classification criterion is done by the classification link. The classification network defined in this way can be mapped as a matrix like any other network.

If the classified objects are visually representable objects, then it is possible to correspondingly enrich the conventional matrix and to convert it into an image matrix. For this purpose, the simple links are replaced by images of the network nodes, ie images of the objects or the classification criteria. It makes sense in many cases to pick out a part of the object that corresponds to the linked classification criterion or vice versa. The method therefore appears to be particularly useful in particular if the objects in question or the classification criteria are present in a subdivided, possibly hierarchical form or in a form that can be combined into higher units.

In the method, the visually representable objects can assume the role of the object as well as - in special cases - those of the classification criterion. Corresponding objects in the role of the object are also referred to below as image documents. An image document is defined as an arbitrary, visually represented or representable object or a collection of several of the same. Typical examples of image documents are a book with illustrations, a book with scanned text pages, a hand drawing, a sketchbook, a photo, a photo collection, the photos of an Internet user or a homepage.

Typical examples of composite tangible classification criteria are keyword sets or tags ^λ, which are combined into meaningful groups like Tagclustern ^Λ. Typical examples of subdivided classification criteria are hierarchical systems, thesauri or ontologies. More or less complexly subdividable and, at the same time, higher units that can be summarized are sets of discrete objects such as websites, locations or physical and conceptual objects. Specially man-made objects such as ancient monuments, monuments or paintings often appear both in the role of the classification criterion and in that of the object, for example when the classification link describes the indefinite or directly demonstrable dependency of an object on other objects (reception or reception) Tradition). The image matrix is understood in principle in the present context as a special form of the conventional matrix. The matrix therefore forms the starting point in their production. It is first enriched with the necessary information to nodes and links and then converted into a picture matrix in a simple step. The enrichment can either be taken directly from the initial data set, or it is stored in a new adjacency list and kept. This new adjacency list serves as a temporary database during editing and analysis of the image matrix. It will be referred to as a 'base list' in the further course. The base list may contain the entire network of output data or only part of it, and must be recreated or updated after each major change.

The following are some basic explanations to the matrix and image matrix. Subsequently, a possible basic list is presented by way of example and its production is explained in more detail. Based on this, the generation of a matrix as well as the associated image matrix is explicated. It deals with the scaling or zooming of the (image) matrix, ie the handling of the possible summaries or subdivisions of the network nodes. Finally, various advantages of the method and the image matrices generated thereby are presented.

The example network of the visualization is that of the reception. As classification criteria it contains subdivided ancient monuments, ie works of art or building complexes; take over the role of the objects (picture) Documents, ie visual sources in which the ancient monuments are represented. For the user of a finished implementation of the presented method, the general workflow when creating an image matrix usually takes place on the basis of a few simple steps (FIG. 6): First, by sorting the matrix (permutation), as many correlating rows and columns of the matrix as possible are brought together , so that a region of particular density of filled cells (edge value greater than or equal to 1) is formed. In a further step, the unneeded rows and columns are filtered away so that only the relevant area remains visible. Finally, the filtered area is converted into a picture matrix by clicking.

In the background, the technical process contains some automatable processes with which the end user does not have to come into direct contact (see Figure 7). They are explained explicitly below.

2. matrix and image matrix

In a matrix, the nodes of a network are represented as rows and columns; the edges (single or multi-valued links) as points or cells. An important difference to the classical network visualization, in which the nodes are represented as points and the links as lines, consists in the radically different enrichment possibilities with additional information. While a network representation is primarily suited to show the location of the nodes of whatever kind, the matrix is especially suitable for visualizing sequential structures such as the dating of objects. The permutation, that is, the sorting and grouping of rows and columns plays an outstanding role. If one forms a network of nodes and links as a matrix, then at the corresponding intersection of the two linked nodes, either one, 0 ^Λ or one, 1 \ depends on whether a link is present or not (FIGS. 2a, 11).

Even this simple form of the matrix is of tremendous benefit, since it can be used to perform arithmetic operations and analyzes in the respective network. The known bandwidth ranges from the extraction of walkabout navigational paths to the establishment of meaningful groups by permutation, i. Swap rows and columns (prior art).

An extension of the simple matrix means weighting the links with a particular value. This is useful, for example, for a network in which the source and destination nodes of the links to groups or hierarchical structures are combined. The value of the matrix cell, hereinafter referred to as the edge, here corresponds to the number of actually allocated links between the respective summaries. The summary and weighting of the matrix rows and columns can be realized, as is provided in the prior art in the "block modeling" in the so-called social network analysis.

If there are three links between the superordinate object (eg a document complex) and the superordinate classification (eg a monument complex) (ie for example three drawings in a sketchbook of different parts of the Pantheon in Rome), the corresponding value is 3 ^λ (Figure 2b ). Strictly speaking, the weighted value of such an edge represents a detail matrix of the individual sub-nodes of the respective complexes (FIG. 2c). From the above cases, there are three possibilities for the matrix: Either instead of the respective edge, CP or '1' (according to link present or not), a value greater than 1 ^{Λ appears} (if the link is a summary of several links ) or a detail matrix (if the partial links are to be shown explicitly).

In order to carry out the step according to the invention from the matrix to the image matrix, the content of the edges is replaced by the image of the linked document (part). In general terms, the mapping corresponding to classified (detail) nodes takes the place of the links between the document and classification nodes (Figure 3).

Instead of a 1 in the matrix, a picture or the text of the linked document (s) will appear. It is advantageous if the corresponding quadrants are referenced in an existing image or present cut out of it.

The individual document (part) s and (sub) classifications can also be combined in the (image) matrix into higher-level (global) or intermediate (metalocal) units. Weighted edges with a value greater than 1 do not correspond in the matrix to a single link, but rather to several links between the connected nodes, possibly combined in several parts. There are basically three possibilities:

The simplest method is to use the detail matrices

(Figure 2c) and their filling with the individual quadrants contained (Figure 4a). The second method is filling the cell with the mappings of the relevant single quadrants, without adhering to the order of the detail matrix - a procedure that makes sense especially with extensive detail matrices, otherwise the images often become too small (Figure 4b).

The third method involves the assembly of the partial representations contained (FIG. 4c) - an often useful application that significantly improves comparability, especially in the case of middle higher-level units. The left part of FIG. 5 shows three details from a sixteenth-century hand drawing code (, Doc template ^Λ ), which are all linked by the classification link with a particular section through an antique building ("Monument *"). In the right part of Figure 5, the three parts are provided as provided by the authors of the Codex. The comparison with the clearly dependent section of another collection of drawings (doc. Copy ^Λ ), which can also be seen in the matrix, is much easier thanks to the assembly in the second illustration.

The major problem with the use of assemblies of this kind is the nature of the overarching question: Strictly speaking, montage possibly represents the link between an ideal parent document unit and the corresponding superordinate classification criterion - a relationship that may possibly even exist in the original data set in this form does not exist, as there are usually only the links between actually existing document (s) and possibly subordinate classification criteria are listed. The image matrix therefore shows itself through the use of montages as an independent product. It is not a pure representation of the existing data, but goes beyond what has been found in the statement. Despite the associated problems, the montage enrichment of the image matrix makes sense in many cases, since a large number of document (s) can also be understood as part of superordinate or completely independent, ideal (documentary) concepts: individual sketches of various historical sketchbooks can be summarized, for example, to a presumed reconstruction project; Fragments of a single representation can be combined for better comparability if necessary to an incomplete Gesamtdar- position. From the increased comparability finally possibly unknown dependencies can be opened (see advantage 2).

3. Description of the basic list

The already mentioned base list ^λ (FIG. 8) or a dynamic equivalent can be present implicitly in the output data set or can be created externally. For technical printing reasons, the basic list of FIG. 8 is shown in three separate partial images (FIGS. 8a, 8b and 8c). The base list contains information about nodes and links in the source dataset. In this case, various relationships between nodes can occur, which are shown schematically in FIG. FIG. 8 shows that the extraction of different record numbers (node IDs) in the base list permits the external answering of simultaneous local, global and metalocal questions or the reconstruction of a tree structure.

In principle, the base list is an admias list enriched with metainformation on nodes and edges of a network, which can serve both to produce scalable (image) matrices and to produce classical network visualizations. (Picture) Matrix like network visualization tion require a so-called nodesets ^λ (set of nodes, group of information about the nodes of the network) and one Edge sets ^Λ (edge set group information on the edges of the network). Both are contained in the base list, or can be generated dynamically from the same. Additional enrichments can serve the better sorting as well as the clearer representation of the respective final product. By combining different basic lists, it is also possible to combine the different network types (such as reception, traditional or tree structure) in a single visualization.

In this sense, the image matrix of a reception network in FIGS. 12a-12c in superposition shows a second network in classical network visualization - the network of the tradition.

In the following, an external base list separated from the output data set will be described by way of example and its production explained. The starting point is a database output (step S1 in FIG. 1) which contains all relevant link relationships of a reception network. In a second step, the resulting simple adjacency list of the links is enriched by node information from another database selection. The procedure is the same for each selected subnetwork. For each type of link in the output data set, a separate basic list can (and should usually be) created.

If the base list is represented as a spreadsheet, it will conveniently contain three column groups (Figures 8a, 8b, and 8c) - one to the left exit node, one to the link destination node, and another to the resulting edges. Each line represents in the list N real existing in the output data set existing link (the, self-self-edge ^Λ ).

The node set, that is, the information about the nodes of the network, can be extracted from the first two column groups of the base list. The edgeset is equivalent to or derived from the third column group.

The first two column groups of the base list (8a, 8b) to the nodes are in four sub-groups divided according to detail below defined summaries, self ^Λ, Parent \, Main ^λ and, Entity2 ^Λ of the respective Linkausgangs- or link target node. Each of the subgroups contains in the first place the corresponding record number ^Λ (or possibly any other node ID), in the second place the so-called Labeistring ^Λ and in third place the so-called "Occurence":

The first column of the four subgroups to the nodes in the base list contains the 'record number' of the source or

Target node or the corresponding node of the corresponding summary (see Figure 9,, RecNo ... ^λ corresponds in Figure 8, Doc ... ^λ , for example, DocSelf \ or, Mon ... \ z. MonSelf ^Λ ):

, RecnoSelf * is the record number of the read out node itself.

, RecnoParent ^λ is the record number of the first parent node in the existing node hierarchy (part-of-link). It serves, for example, to display the tree structure of a document in a network visualization, in addition to reception and transmission. For the aggregation of superordinate units, it plays only an indirect role. , RecNoMain ^λ is the record number of the node at the top of each node hierarchy, which coincides with the 'global' document unit. During read-out, the node hierarchy is tracked up to a marker determination for this purpose. Each node at the top of a document tree is accordingly marked as, Main ^λ before being read out.

, RecNoEntity2 ^λ is the record number of a possibly existing ideoyncratic, meaningful, metalocal ^Λ unit of the document, which is marked with the help of the marker, Entity2 ^λ . As with the 'RecNoMain', the node hierarchy is also tracked upwards when reading up to the marker determination.

Thus, the node identifications mentioned in Figure 9 z. For example, RecnoSelf ^λ refers to a particular image in a book, RecnoParent ^Λ refers to the parent page directly in the book, RecNoMain ^λ refers to the book itself, and RecNoEntity2 ^λ refers to a multi-page catalog entry in the book.

The second column of the four subgroups of the nodes in the base list (FIG. 8) contains the so-called Labeistring ^x . It serves to enrich the respective nodes in the matrix with useful information.

In general, that is, if the source and destination nodes are of different types, it is convenient to define two different formats for the Labeistring ^Λ . In the following, for example, a meaningful Labeistring for (picture) documents and then another for the classification (here antique monuments) will be explained by way of example. Labeistring Document:

At the beginning of the bibliography of the (pictured) documents are the already presented record numbers, which are used for the summary of superior units (see Figure 9).

, Type ^Λ expediently specifies the node type of the read-out entry, that is, in the case of documents, for example, whether it is a single object, a publication or a photograph.

LabelSelf contains only the name of the node itself. It is necessary, for example, if the tree structure of a document is to be visualized as a network without showing redundant information at the nodes of the tree.

'Label' contains the complete name of the node, which may also include information from superior or, as in the case of the document location, suitably hypotactically linked nodes, eg, for individual objects, the label more or less corresponds more or less to the sequence 'place / institution / Department: Codex / Folio / Quadrant "and for publications of the series" Short Name / Position ".

, DateName ^λ contains, for example, the name of the (first) time range used for dating. (Of course, documents can also include the date of origin be rivaled, ie, multiple times, for example, in divergent research opinion.)

, begin ^Λ and, end ^Λ contain the numerical start and end times belonging to the DateName ^λ which are required for sorting in the form +/- YYYY: MM: DD (= year: month: day).

For example, lstArtist ^λ contains the first person associated with the document under the condition 'artist'. (Of course, all connected artists or even other people could stand here.).

'ImgFile' contains the reference to the image file corresponding to the database entry, or in the case of documents reprinted only secondarily, the reference to the image file of the first dependent document, provided that this is a photographic copy.

In addition to the above-mentioned components, the label string of the documents could also be enriched by further additional information - for example, by GIS information on the locality.

Labeistring Classification:

RecnoSelf I RecnoParent | RecnoMain | RecnoEntity2 | LabelSelf | label

The bibliography of the classification criteria corresponds to that of the (image) documents with regard to the basic data. If the classifications are more complex structures, such as antique monuments or documents in the example case, then the corresponding Labeistring can be as rich in information as the Labeistring to the document. In this case, there are no additional enrichments for sorting included. The function of the contained fields corresponds to the explanations of the label of the documents.

The third column of the four subgroups of the nodes in the base list (FIG. 8) contains the so-called 'occurrence' of the nodes. It indicates the relative frequency of the corresponding entry in the subgroup. It is collected by simply counting the similar, record numbers ^λ in the first column of the subgroup. It corresponds to the output node OUT degree or the destination node IN degree.

It should be noted that the 'Occurence' must be recalculated to a subset of the output data set in the case of a limitation of the base list ^λ . Simply reading out the total number of links to an entry from the output data set may make no sense because the restriction does not have to correspond to the existing data of the output data set.

In addition to, Recordnumber ^Λ ,, Labeistring ^Λ and, Occurence ', the subgroups of the two column groups to the nodes in the base list can also contain information on images and sorting.

The fields, Image ^Λ (and, Imgext ^Λ ) in the column subgroup, DocSelf ^Λ (Figure 8), contain the link to the respective image file or the respective section of an image file, which is important for the image matrix. The record number specified therein may differ from that of the node itself, for example if the image file is from a reprographic copy - a feature that may be marked by a marker in the image matrix. The 'Sort' columns in the column groups 'DocMain' and 'DoCEntity2' (Figure 8) come from the sorting of matrices created from the base list. If necessary, the information is imported back into the basic list by means of a macro. This makes sense, since the complex, partly manual sorting of the matrices, for example, can be easily downwards, that is, from the, global ^λ summary, main ^λ to 'metalocal' or 'local' summary, entity2 ^λ or , Self ^Λ can be inherited.

The third column group of the base list (Figure 8c) to the edges contains up to nine subgroups (3 link starting points by 3 link targets) from the three summarization levels, Seif,, Main ^Λ and, Entity2 ^λ . Only the two ratios, DocMain-MonMain 'and, DocEntity2-MonSelf ^{Λ are shown} .

Each subgroup contains the corresponding edge in the first column, which is created by simply concatenating the corresponding record numbers. The second column of each subgroup contains the, Edgeoccurence ^λ , which is calculated in the same way as that of the individual nodes. For example, the Edgeoccurence ^Λ can serve as an indicator of the density of various classification complexes in a large document. The statement quality is of course variable, since, for example, a single good drawing can exceed many bad sketches in importance. 4. Production of the basic list (step S2)

4.1. Reading the raw adjacency list

The raw form of the database output corresponds in the case of simple links between source and destination nodes, for example, the following form:

Database selection "Edges": Link root link targets ...

RecnoDocl RecnoMonl RecnoMon2 RecnoMon3 RecnoDoc2 RecnoMon4 RecnoDoc3 RecnoMon2 RecnoMonδ

The required result in the database must contain only the starting nodes of the links. They appear in the output in the first column. The following columns show the goals of the links. Both link home nodes and link destination nodes are represented exclusively by their ID (record number, primary key or URI ...).

In the next step, the raw database output of the link relations is converted into a two-column form:

Raw Edgelist (adjacency list): Link Root Link Target RecnoDocl RecnoMonl RecnoDocl RecnoMon2 RecnoDocl RecnoMon3 RecnoDoc2 RecnoMon4 RecnoDoc3 RecnoMon2 RecnoDoc3 RecnoMon5 Each link exit node thus faces a single link destination node. Each line thus contains a single link ratio, which explicitly exists in the database in this form. If, for example, the links in the output data set are represented as independent event nodes (or as a crosstab in the case of a relational database), the result of these events can also be read out directly. The output then immediately corresponds to the two-column form.

4.2. Enrichment of the raw adjacency list

In the further course, the two-column adjacency list is enriched by additional node information. On the one hand, this allows to summarize the nodes and link relations to global and metalocal units in the matrix as well as to sort the (image) matrix according to criteria such as designation, location, date or artist of the respective nodes.

The enrichment of the raw adjacency list is done by simple database readings of all relevant nodes (eg documents and monuments) in the form of the above-described Labeistring ^Λ . For this purpose, it is usually not necessary to create a specially adapted result in the output data set; All documents and monuments of the output data set are simply read out. From the finished outputs, a macro is then created, which replaces the record numbers in the raw adjacence list (, raw edge list ^y ) with the complete, read string ^Λ . The selection of the relevant entries then results automatically from the record numbers existing in the raw adjacency list. In a further step all existing record numbers, that is, RecnoSelf \, RecnoParent ^Λ , RecnoMain ^λ and, RecnoEntity2 ^Λ are enriched in the accumulated list by the same macro again with the Labeistring.

The end result is the raw form of the basic list described above (FIG. 8).

5. Preparation of the matrix (step S3)

As a matrix visualization tool, a good conventional spreadsheet may conveniently be used. However, it is also possible to implement the described method in a genuine matrix application (to the absence of such a method, see Daru, Myriam: Jacques Bertin and the graphic essence of data, Information Design Journal 10 (1) 2001 pp. 20-25). The only real limitation of the spreadsheet over a desirable matrix tool is the existing limitation of the number of columns to 256. All other limitations primarily concern the convenience of the user interface as well as the computational speed which will certainly be significant when adapting the application to the desired purpose could improve.

A schematic of the basic procedure in the production of a matrix from the basic list is shown in FIG. 10 (detail from FIG. 7):

First, a node set is extracted from the base list for the sets of link output nodes and link destination nodes. The classification criteria node set usually gives the columns of the matrix, the item nodeset the rows. Both nodesets are composed of the information that exists in the respective subgroup of the base list. is present. Primarily only the record number (ie the ID) of the corresponding nodes is necessary. All further information is used for later sorting of the matrix or for quick identification of the entries.

If globally or metalokally summarized main and entity2 nodesets are extracted from the base list, existing redundancies are filtered out before insertion into the matrix, so that each classification criteria or object complex occurs only once in the corresponding nodeset. After filtering and possible pre-sorting, the nodesets are copied into an empty table (see Figure H).

For metalocal Entity2 and local self-matrices, the label ^{Λ of} the nodes contained in the Labeistring ^λ may be split into different cells - accordingly

"Book" I "Chapter" I "Figure" instead of "Book / Chapter / Figure" - to sort polyhierarchically.

The edge set usually does not have to be extracted from the base list in the case of matrix production (in contrast to the classic network visualization). The corresponding subgroup in the third column group (FIG. 8c) is sufficient despite its existing redundancies.

Filling in the matrix is done by simply checking whether the corresponding record number combination of source and destination nodes exists in the respective subgroup of the boxes in the basic list. In each cell of the matrix table to be filled in, a command according to the following example (in Excel 2002 ™ format) is used: = IF ((COUNTIF (e; (CHAIN (x; "$";y)))>0); (CHAIN (x; "$"

Y)), 0)

e denotes the corresponding edge column in the base list (eg, [baselist.xls] edges ^x ! $ AP: $ AP); x and y are variables designating the respective output and destination nodes, respectively. In the example shown, the link output node record number is in cell x (eg $ EU22), the link destination node record number in cell y (eg ET $ 20).

Once the matrix has been computed, the spreadsheet may convert the dynamic values into fixed values, remove zeros, and assign appropriate conditional cell formatting (e.g., black background if cell content is not 0). A finished matrix 5 is illustrated by way of example in FIG. 11.

6. Production of the image matrix (step S4)

6.1. main steps

Once the matrix 5 has been completed, the production of the image matrix 6 is technically divided into two sections.

First, the so-called Edgelabel ^{λ are} created, which consist either of the respective link-out node, ie the document (part), or if the, edge-occurrence ^{λ has} a value higher than one of several of them.

The second section, after creating the Edgelabel, concerns the actual visualization of the image matrix. For this purpose, the matrix is first sorted, filtered and if necessary transposed. In an automatic step, the actual image table is finally generated (FIG. 12). FIG. 12 shows, by way of example, a detail of the image matrix, which in practice can be significantly larger and can comprise, for example, 200 columns and 2,000 rows.

The complete general procedure follows the illustration in FIG. 7. Due to the complexity of the representation, it is important to note that all detours shown can be automated. For the user (eg researcher), this means that, after appropriate implementation of the method, he can, if necessary, generate an image matrix from a matrix at the push of a button.

6.2. Generation of 'edgelabel'

In the following, the generation of the "Edgelabel" will be explained. Like the basic list, they are created only once for all possible edges. Alternatively, it would also be possible to create only the necessary edgelabel 'on the fly' at the time of visualization. The latter variant is when using the invention in a computer network, for. As on the Internet, advantage to limit the data processing effort on the processing of the currently desired information. It should be noted that the edgelabel ^Λ must be created separately for all node summaries of global, metalocal and local type. This is necessary because the edgeoccurence ^{λ of the} same named edges can differ in the different summaries (see FIG.

As mentioned, the content of a cell, ie an edge in the matrix, does not necessarily correspond to the direct link ratio between the respective summarized objects and classification criteria in the database. Rather, especially when global or metalokal summarized Matrix rows or matrix columns, multiple edges in one edge.

The edge between folio and monument in FIG. 13 represents, for example, in a locally combined matrix (DocSelf matrix *) only the direct (also monovalent) link between folio and monument that also exists in the dataset. In a metalokally aggregated matrix ("DocEntity2" matrix ^Λ ), the same edge between folio and monument represents a total of three links in the dataset: the Folio Monument link, as well as two more links between the quadrants and the monument parts.

It follows that the matrix mainly in the form of framed zusammenge- ^x is a standalone product that in its

Meaningfulness can surpass the content of the dataset in its conventional accessibility.

The image matrix will be coded as HTML in the following. The content of the, Edgelabels ^Λ is therefore defined as an HTML table cell:

Table cell contents (general): <a href="Edgelink"> <img src = "EdgeImage" border = "0" alt = "EdgeAltText"> <br> EdgeLabel </a>

Contents of the table cell for Occurence = 1: <a href="... /Database?RecNo"><img src = "... / RecNoDocument. Jpg or ... / RecnNoArchetyp. Jpg" border = "0" alt = "Label string of RecNoDocument and EdgeLabel"><br> node Recno </a> Contents of the table cell for Occurence> 1: <a href="DetailMatrix.htm or detail overview. Htm ^ΛX> <img src = "Detail ^matrix . jpg or DetailUberblick.jpg "bor- der =" 0 "alt =" Label string of RecNo (Parent) Document and Edgeθccurence ">

<br> Edge RecNoDocument $ RecNoMonument </a>

The contents of the table cell of each edge are given three elements in the image matrix next to the name (EdgeName): a picture (EdgeImage), an explanatory text

(EdgeAltText), which may appear when you hover over it in the online version and a link (EdgeLink) that allows you to navigate back to the database.

If the, Edge-Occurence ^Λ equals 1, the filling of the corresponding parts is very simple, as all information from the Nodeset or Edgeset can be found in the basic list:

The, EdgeLabel ^λ corresponds to the label of the associated link output node, ie, for example, the document (part) s.

The, EdgeImage ^Λ corresponds to the respective image or that of the reprographic template (possibly indicated by a frame or the like).

The, EdgeAltText ^λ contains any information from the respective Labeistring and for better control the name of the Edge (Recno $ Recno).

The 'EdgeLink' opens the link home node, ie document (part) in the database. Exceeds the Edge-Occurrence ^Λ the value 1, so a detailed matrix, which in turn contains the relevant link output node takes the place of the single link output node in the table cell ideally. However, as the images generally become even faster too small, it is advisable to show an overview image instead of the detail matrix, which contains only the filled cells of the detail matrix (FIG. 14).

The overview image replaces the single image in the 'edgeimg' of the table cell. It must be specially created for all edges of multiple occurrences, for which purpose a concordance is created from the base list in which all record numbers of the link output nodes, as well as their image reference, are collected at the multiple occurring edge. From the concordance, one HTML file is then created for each edge. It carries the name of the edge and allows navigation from the individual contained nodes back to the output data set. Finally, the HTML version of the overview image is converted to an image file using a special tool (e.g., Html2jpg ™) for insertion into the HTML version of the image matrix.

The finished 'Edgelabel' for values greater than 1 contains as name the original name of the edge in the form, recno $ recno \ as, edgeimg ^Λ the overview image as well as, edgealttext ^λ the value of, Edge-Occurence ^{λ of} the edge and the, label ^{λ of} the parent document complex. The edge link ^λ expediently does not directly refer to the output data set, since the corresponding edge does not always represent a real existing relationship but rather a summary of several such relationships. The link therefore expediently opens the interactive HTML version of the overview image, from which individual quadrants one can then navigate into the output data set. 6.3. Generation of the image matrix

If the Edgelabels have been created for edge values equal to one and larger than one, the matrices are enriched with the Edgelabels with the help of several macros. The first two macros replace the name of the edge in the matrix cell with the HTML table cell. The third macro generates the HTML overview panels and the fourth generates the corresponding image files.

The enriched matrix may subsequently be extracted from the spreadsheet, e.g. with a good HTML editor (such as Adobe Dreamweaver ™) are imported into an HTML file and displayed in the browser as a picture matrix.

An advantage of the procedure described is that after enrichment in the spreadsheet, the matrix retains its original shape, that is, each filled cell remains black after enrichment with the edgelabels

Box appears. As a result, the matrix can also be easily processed in an enriched form and quickly converted into an image matrix.

As an alternative to this relatively static encoding of the matrix in a spreadsheet and the image matrix in HTML, it is also possible to maintain the overview images or the corresponding detailed matrix of multiple edges within the (image) matrix in interactive form. This would not only guarantee a more comfortable connection to the output data set; This also makes possible a more comfortable processing of the data volume, such as the merging of duplicate entries and the assignment of links between the individual by clicking and dragging ^λ or, pointing and clicking ".

6.4. Zoom the matrix (scale)

An important problem in the production of suitable image matrices for the analysis of networks of classified image documents is the selection of meaningful summaries of the possibly hierarchical or grouped classifications as well as possibly subdivided image documents themselves. The choice of the respective summary determines the possible size of the matrix ( FIG. 15):

If all the directly linked nodes of the classification or the documents (locally) stand in a matrix, then large node complexes with numerous linked individual nodes may claim several hundred or even a thousand columns or rows. If one summarizes the classification criteria. By contrast, document complexes are grouped together in the largest possible (global) unit, so each complex occupies only one line.

A matrix in which the documents are mapped only locally would, in the case of a dataset with 10 000 classification links, comprise up to 10 ^λ 000 document lines and would therefore not be useful for direct human interaction. Moreover, in such a matrix, it would be impossible to create areas of meaningful density for an image matrix because a majority of the lines would typically contain only a single or very few filled cells. On the other hand, a matrix in which the documents are mapped globally prevents numerous detailed questions, since so many links are combined in many cells, that a meaningful comparison is prevented by too much density.

The global summary is particularly problematic in reviews such as exhibition catalogs or art corpus works, which contain, for example, not just one document (e.g., a city map) with different classifications (e.g., different monument representations), but several hundred of them. In a globally summarized row, hundreds of identically classified representations (for example, of a single monument) would be collected in a single matrix cell - a fact that makes no sense for reasons of clarity, nor does it spread to the local nodes.

One possible solution to both problems, if documents and classifications are hierarchically subdivided in several stages, is the introduction of metalocal units (i.e., for example, book / chapter / place instead of book / place). The metalocal unit, and thus the multi-level hierarchical subdivision of documents in humanities databases as a whole, finds its primary purpose here.

The metalocal unit encounters in the image matrix (as in any other form of display of the data) both the over-globalization, as well as the excessive local fragmentation.

In the case of extensive catalogs or corpus works, for example, the individual catalog entries within the publication can be summarized: Consequently, more detailed questions, such as the classification of individual catalog entries in a direct visual comparison, are possible. Applied on a large scale, the introduction of corresponding metalocal units leads to image matrices whose scope is on the one hand tolerable to the human eye, but in which on the other hand detailed documentary questions are also possible within the framework of further analysis. At the beginning of the analysis, it usually makes sense to juxtapose the local mapping of the classification with a meaningful metalocal summary of the documents.

In principle, the rule of thumb is that in case of doubt, one should rather forego the preliminary summary of metalocal units, since individual related entries can also be meaningfully grouped later in the matrix. It is therefore sufficient to deposit only so many metalocal summaries that a meaningful starting size of the matrix is created. Other meaningful metalocal units may later be recognized and deposited as part of the analysis.

Zooming the matrix follows this procedure in discrete steps:

First, meaningful groupings and fragmentations are stored in the output data set during the analysis.

The new metalocal units become accessible in the following generation of matrices (refresh).

Alternatively, it is possible to liquefy this discrete, step-by-step process by making the possibly existing tree structure of the classification such as the documents completely dynamically accessible at the edge of the matrix, similar to the directory structure in the file explorer of an operating system (see FIG pictogram used in FIG. may be the subject of registered trade marks). As in the Windows Explorer ™ or Mac Finder ™, it is also possible to zoom in and out of selected areas of the matrix by unfolding and closing.

An implementation in this form is particularly meaningful if both the classification criteria and the subdivision of the classified documents themselves correspond to the form of strict trees in the sense of graph theory and these trees are not broken up by permutation of the parts (see Advantage 1 ).

7. Further advantages of the invention

Advantage 1: Easier handling of the ambivalence of the higher unit:

Simultaneously visible image information in the image matrix allows the recognition and production of meaningful sorts or groupings (permutations) of several individual nodes and node complexes. It is possible to play with the more or less existing subjectivity of the subdivision of the classification criteria or the objects themselves. The roots of the possibly existing strict node trees are virtually cut off for this purpose. Information can be found in the

Sequence can be sorted differently and combined into alternative meaningful units. This creates new meaningful groupings of nodes, which are not necessarily oriented to the usual physical distribution of the represented objects (eg drawings of an artist from different collections, an assumed reconstruction project or the like). The found groupings are first compiled by permutation in the (image) matrix. The visual properties of the image matrix have an advantageous effect in the context of this process, especially in the case of manual permutation, since the sorting criterion is always in view.

Boundary lines mark the groupings together directly in the (image) matrix. Alternatively, however, they can also be firmly integrated into the output data set as cognitive concepts ^λ (ie, for example, as virtual objects). The, cognitive concepts ^λ are deposited as a set of linked alias nodes and represent the newly found groupings as further use as virtual (image) documents. So they offer an alternative to the given physical order without destroying them. Independent, cognitive concepts ^Λ according to this definition can also serve to deposit the assemblies discussed above in the output data set.

Advantage 2: Easier approximation to the justification of the correlation of (image) documents:

Possible reasons for the similarity of visual objects are: a) direct dependence, b) indirect dependence, c) external reasons (such as a prominent vantage point in the case of similar landscapes) or d) chance.

Randomness can be more easily excluded the stronger the correlation of the similar visual objects. All other causes are usually much harder to distinguish. Due to the contained image information, due to the two-dimensional matrix order and due to its permeability, the image matrix proves to be an extremely useful tool. Detected direct dependencies (historical events) or other more precisely specifiable relationship between two mappings can be stored in the image matrix, for example by drawing left-hand arrows (eg by 'clicking and dragging' with the mouse). With appropriate implementation, the image matrix can thus serve as a convenient user interface for processing the output data quantity.

Advantage 3: Extraction of time-bound phenomena related to the objects such as the classification criteria:

If objects, as in the example discussed above, are post-antique (picture) documents, the classification criteria are anti-monuments, then a unified approach to art history and classical archeology results. Art history is devoted to the time-bound phenomena of (image) documents based on the classification criteria. Classical archeology is dedicated to the time-bound phenomena of the classification criteria on the basis of the (picture) documents.

The image matrix serves the following purposes:

a) Extraction of the history of the objects or the classification criteria (storytelling). Examples are the development of hand sketchbooks or the micro-history of a building part.

b) Visual check whether the classifications selected when filtering the matrix are actually relevant to the problem (relevance control).

c) In the case of the history of the classification criteria, the image matrix serves to exclude dependent representations, since Here only the respective first representation of a series of copied representations of relevance is. The exclusion criteria are direct dependencies, which only become apparent in the image matrix (see advantage 4).

d) Improvement of the relative dating of blurred or undated objects. Vagically dated nodes (e.g., dated, 17th century *, or terminus ante or post) may be more accurately ranked based on the image information present in the overview.

Advantage 4: Easier to answer implicit visual detail questions:

Since in the image matrix several objects classified according to the same criteria can be compared at a glance, it is also possible to classify unclassified, i. implicitly, not literally in the classification to examine explicit details of the objects.

For historical (image) documents classified according to the monument shown, it is e.g. it is possible to read the history of a particular window in a wall, without the window being explicitly part of the classification criteria.

In general, visual detail questions in this form are especially useful if the superordinate classification criterion is also of a visual nature.

Advantage 5: Facilitate Data Analysis and Revision:

The image matrix facilitates data analysis and revision, for example, duplicate but differently named as well Unidentified objects can be detected by inspection and possibly geraergt or otherwise related. If the classification criteria are of a visual nature, corresponding candidates automatically accumulate in the same matrix columns or rows.

8. Other applications of the invention

Other applications of the invention include, for example:

(a) applications for analyzing complex amounts of data;

(b) administration and processing of (visual) data (in this context, the procedure also facilitates bibliometric quality assurance);

(c) visual and art sciences, especially in the area of reception, tradition and memnosyne. In addition, the method also proves to be very useful in the relative dating of blurred objects (Baroque painting, drawings, archeology and the like);

(d) securing copyright on images;

(e) data processing in medical imaging (such as the simultaneous observation of the development of similar cancer metastases in the context of multiple patients and / or organs), biology (research on the phylogeny of the species and evolution in general), art science (exploration visual Similarity of individual items) or in other technical areas, eg. B. air reconnaissance (simultaneous display of aerial sequences of several locations); and (f) Administration of implicitly or explicitly classified image data sets (implicitly: image search engines, eg explicitly with tags and clusters).

The features of the invention disclosed in the above description, the drawings and the claims may be of importance individually or in combination for the realization of the invention in its various embodiments.

Claims

claims

1. A method for producing an image matrix (1), comprising the steps of:

- providing a network having a set of link output nodes (2) and a set of link destination nodes (3) with links (4) therebetween, - forming a matrix (5) with rows and columns, the link output nodes being assigned to the lines and the link destination nodes the columns or vice versa, and

Placing visual representations (6) of the left exit nodes (2) or the link destination nodes (3) in place of the links in the matrix to yield the image matrix (1).

2. The method according to claim 1, characterized in that

Link-out nodes (2) Classifiable objects and link-destination nodes (3) Classification criteria are or vice versa.

3. The method according to claim 2, characterized in that

- Objects and / or classification criteria are objects from a set of persons, locations, time ranges, physical objects, conceptual objects, events and periods.

4. The method according to claim 2 or 3, characterized in that - objects and / or classification criteria are represented by individual nodes or as a group of nodes.

5. The method according to any one of claims 2 to 4, characterized in that

- In a multi-valued link from and / or to a multipart article or a multi-part Klassifikationskri- tium a detail image matrix (7) is placed.

6. The method according to any one of claims 2 to 4, characterized in that

- In the case of a multi-valued link from and / or to a multi-part article or a multi-part classification criterion, a one-dimensional overview table (8) is placed.

7. The method according to any one of claims 2 to 4, character- ized in that

- If a multi-valued link from and / or to a multi-part object or a multi-part classification criterion, a picture montage (9) is placed.

8. The method according to any one of the preceding claims, characterized in that

- Multi-part, in particular hierarchically subdivided objects or classification criteria are folded in the matrix in an input signal into a plurality of matrix rows or matrix columns.

9. The method according to any one of the preceding claims, characterized in that

- multi-part, in particular hierarchically subdivided objects or classification criteria in the matrix at a

Input signal in a matrix line or matrix column.

10. The method according to claims 2 to 9, characterized in that

- Individual objects and / or classification criteria in the image matrix can be connected by means of more precisely specifiable relationships.

11. The method according to any one of the preceding claims, characterized by the step:

Outputting at least part of the image matrix on an output device, in particular on a screen or a display

Printer, or in a file.

12. The method according to any one of the preceding claims, characterized by the step: - data processing of the data of the image matrix, and

- Storage of the processed data and / or output of a modified image matrix.

13. The method of claim 12, wherein: the data processing comprises a data input, an image recognition, a correlation and / or a rearrangement of data of the image matrix.

14. The method of claim 13, wherein: - the storage is provided in an amount of data from which the provision of the network takes place.

15. The method according to any one of the preceding claims, characterized in that - the method runs on an electronic data processing system.

16. The method according to any one of the preceding claims, characterized in that

- Additional information is placed on the matrix elements of the image matrix (1).

17. An electronic data processing system comprising a processor and a storage medium, wherein the storage medium includes software that causes the processor to carry out the method according to any one of the preceding claims.

18. A storage medium comprising software for an electronic data processing system with a processor, wherein the software causes the processor to carry out the method according to one of the preceding claims.