16 Querying GML distances between spatial objects and calculating relative direction. Some other forms of spatial predicates are realized as a set of functions. The basic syntax of GML-QL is the same as that of XQuery, with added spatial functions. The following are some examples of GML-QL queries. Query 1: List the name, population, and area of each country for the le “country.XML”. FOR $c IN document(“Country.XML”) RETURN <Country> <GML:name>$c/Name/text()</GML: n ame > <pop>$c/pop</pop> <area>Area($c/shape)</area> </Country> Query 2: The St. Lawrence River can supply water to the cities which are within 300 km, if needed. List the cities which can use water from the St. Lawrence. This query illustrates buffer analysis and spatial join operations. FOR $r IN document(“River.XML”) $c IN document(“City.XML”) WHER E overlap(buffer($r/Shape,300), $c/ Shape) == 1 RETU RN <CityName> <cname> $c/Name </ cname> </CityName> Both queries use expressions similar to those of XQuery. In the rst example, document function opens the country. XML document and binds the value of every country element in that document to variable $c. Finally, the result is constructed as dened in the return clause. The values obtained from the query, which is bound to the variable, can be used to construct new elements in the result if necessary. In this example, a function, area(), is used to calculate the area of a country. The second example illustrates buffer analysis and spatial joins operations by using overlap() and buffer() functions. Although Vatsavai (2002) mentioned that spatial operators in this query language are imple- mented as a set of functions, the details of these functions were not given. We therefore suppose that this query language has neither a particular application nor an implementation. Nevertheless, it offered a novel and interesting approach to dene a GML query language based on XQuery and OGC-SQL (Open Geospatial Consortium, 1999) spatial operators. CXQuery (Constraint XML Query Language) CXQuery or Constraint XML Query Language (Chen and Revesz, 2002) is a declarative, Datalog- style language for querying and updating XML documents. It employs the syntax and semantics of constraint query languages (Kanellakis et al., Domain Operator Topologically closed geometries Disjoint(a,b) ⇔ a ∩ b = ∅ a and b applies to the A/A, L/L, L/A, P/A and P/L groups of relationships. Touches(a,b) ⇔ (I(a)∩I(b) = ∅) ∧ (a ∩ b) ≠∅ a and b applies to the P/L, P/A, L/L and L/A. Crosses(a,b) ⇔ (dim(I(a) ∩ I(b)) < max(dim(I(a)), dim(I(b)))) ∧ (a ∩ b ≠a ) ∧ (a ∩ b ≠b) a and b applies to the A/A, P/A,L/A,L/L Within(a,b) ⇔ (a ∩ b = a) ∧ (I(a) ∩I(b) ≠ ∅) a and b applies to the A/A, P/A,L/A,L/L Contains(a,b) ⇔ Within(b,a) a and b applies to the A/A, P/A,L/A,L/L Intersects(a,b) ⇔ not.Disjoint(a,b) Table 1. Denition of predicates 17 Querying GML 1990). The input of a CXQuery is a set of XML documents. The output of a CXQuery query is also an XML document. When CXQuery is used to dene views, the query result is not material- ized. A CXQuery expression contains a rule head and a rule body, with a “:-” symbol between them. The rule body contains a set of predicates, which are separated by semicolons. The semicolons stand for the logical operation “and”. To simplify the CXQuery expression, it employs a subset of XPath functionality to navigate the hierarchical structure of XML documents and to avoid namespace con- icts. Since most XML documents exchanged in e-Business have relatively restricted structures, CXQuery considers those XML documents that have internal DTD denitions or have external DTD denition connections. Due to these difculties, to date there is no query language proposal which supports querying spatial XML documents. Since both CXQuery and many constraint query languages are based on Prolog, they can be easily combined. Since constraint query languages can express spatio- temporal queries, the combination leads to a query language for XML documents that contain spatio-temporal data. Moreover, combination can be easily implemented on top of a constraint database system. Query 3 shows a spatial query: Find all build- ings located in citycampus and belonging to the Computer Science department. citycampus(id,constraint):- document(“citycampus.XML”), citycampus(id, departments, buildings, BoundedBy), constraint(x, y, BoundedBy); Building(name, dept, constraint):- document(“campus.XML”), Building(name, dept, spatial), constraint(x, y, spatial); Building(name, dept, constraint):- Building(name, constraint), citycampus(id, constraint), cont ains(citycampus/constraint,Building/ constraint), Building/dept = «Computer Science». The rst two rules construct the constraint representation of the spatial data from the XML documents. The third rule uses a spatial func- tion contains() to test the spatial relation of two spatial objects. One way in which CXQuery improves upon XQuery is by specifying schemas for the results of queries. Chen and Revesz (2002) claim that query results without schemas are limited for dening views, integrating data, updating, and further querying. XQuery can query the results of a query without a schema provided. The main focus of this query language is to provide schema information in the query result. Since CXQuery is derived from a constraint query language and the fact that constraint query languages can express spatial-temporal queries, the combination leads to a query language for XML documents that contains support for spa- tial-temporal data. Gquery Gquery (Boucelma and Colonna, 2004) is yet another GML query language based on XQuery. Unlike GML-QL, Boucelma and Colonna (2004) dene a set of Gquery-specic spatial operators and basic data types. Its data types are polygon, line and point, in the same way as the basic data types dened for GML. The spatial operators can be classied into three groups: operators that return boolean type (equal, inside and cut), operators that return oat type (distance, perimeter and length) and opera- tors that return spatial type (convexhull, center, intersection). 18 Querying GML Query 4 is an example. It obtains the intersec- tion point between a road and a river: for n in city return intersection(n/road, n/river) Gquery is designed for use in a particular mediator architecture. It provides an integrated view of the data supplied by all sources, and Gquery makes it possible to access and manipulate integrated data. conc Lus Ion Currently, there is a large set of query languages over XML. Although each one is based on differ- ent algebra and data models, all of them have the same aim: to query semi-structured data. There are fewer query languages for GML documents. Since GML is an XML encoding, the features of XML could be applied to GML. With this, a GML query language should extend a query language over XML with spatial features. In fact, in this chapter we have discussed four query languages over GML. The rst one is a novel extension of a previous query language over XML. It is based on a robust data model and algebra and it offers all the features of an XML query language and a wide set of spatial opera- tors. Since it was the rst approach in this area, it has inspired other query languages (Chung et al., 2004). The other three approaches are an extension of XQuery, with different aims and perspectives. The rst of these, GML-QL, was the rst novel approach of a GML query language based on XQuery. Since the literature about GML-QL is rather scarce, we suppose that this query lan- guage has neither a particular application nor an implementation. Furthermore, details about spatial operators and functions were not given by Vatsavai (2002). Although the second of these, CXQuery , is based on XQuery, it offers an interesting approach for a spatial query language over GML. CXQuery allows to query and update XML documents us- ing the syntax and semantics of constraint query languages. This query language is currently the best approach over GML. The last approach, Gquery, denes a set of spatial operators for GML. It is a specic ap- proach to be applied in a particular mediator architecture. In conclusion, GML can represent database resources on the web, etc. which can be queried with a specic query language. Query languages over GML are a reality. r eferences Abiteboul, S., Quass, S., McHugh, J., Widom, J., & Wiener, J. (1997). The Lorel Query Language for Semistructured Data. International Journal on Digital Libraries, 1(1), 68-88. Beech, D., Malhotra. A., & Rys, M. (1999). A Formal Data Model and Algebra for XML. http://www-db.stanford.edu/dbseminar/Archive/ FallY99/ malhotra-slides/malhotra.pdf. Boucelma, O., & Colonna, F. (2004). Mediation for Online Geoservices. In 4th International Workshop on Web and Wireless Geographical Information Systems. W2GIS 2004. Korea. Chen, Y., & Revesz, P. (2002). CXQuery: A Novel XML Query Language. In Proc. of International Conference on Advances in Infrastructure for Electronic Business, Science, and Medicine on the Internet (SSGRR’02). Chung, W., Park, S., & Bae, H. (2004). An Exten- sion of XQuery for Moving Objects over GML. ITCC . Proc. Of the International Conference on Information Technology: Coding and Comput- ing. IEEE. 19 Querying GML Córcoles, J. E., & González, P. (2001). A Speci- cation of a Spatial Query Language over GML. ACM-GIS 2001. 9th ACM International Sympo- sium on Advances in Geographic Information Systems. Atlanta (USA). Deutsch, A., Fernandez, M., Florescu, D., Levy, A., & Suciu, D. (1999). XML-QL: A Query Language for XML. Computer Networks, 31, 11-16. Kanellakis, P. C. Kuper, G. M., & Revesz (1990). P. Constraint Query languages, Symposium on Principles of Database Systems. Open Geospatial Consortium. (1999). Simple Features Specication For SQL, 05-1341. Open Geospatial Consortium. Retrieved 13th January, 2005, from http://www.opengeospatial.org Open Geospatial Consortium (2003). Geography Markup Language – GML. Retrieved 13th Janu- ary, 2005, from http://www.opengis.org/techno/ documents/02-023r4.pdf. Robie, J. (1998). The design of XQL. Retrieved 13th January, 2005, from http://www.w3.org/style/ XSL/Group/1998/09/XQL-design.html. Vatsavai, R. (2002). GML-QL: A Spatial Query Language Specication for GML. Retrieved 13th January, 2005, from http://www.cobblestone- concepts.com/ucgis2summer2002/ vatsavai/ vatsavai.htm. W3C (1998) XSL. Retrieved 13th January, 2005, from http://www.w3. org/TR/REC-XML. W3C (2001). XQuery: A Query Language for XML. Retrieved 13th January, 2005, from http://www. w3.org/TR/2001/WD-XQuery-20010215. W3C. (2005). Extensible Markup Language – XML. Retrieved 13th January, 2005, from http://www.w3c.org/XML/ key t er Ms Feature: A feature is an application object that represents a physical entity, e.g. a building, a river, or a person. A feature may or may not have geometric aspects. Markup Language: Language which com- bines text and extra information about the text. The extra information is expressed using markup, which is intermingled with the primary text. Query Language: Computer language used to make queries into databases and information systems Semi-Structured Data: Data with incomplete structure. Data are directly described using a simple syntax, e.g. XML, GML, etc. 20 Chapter III Image Database Indexing Techniques Michael Vassilakopoulos University of Central Greece, Greece Antonio Corral University of Almería, Spain Boris Rachev Technical University of Varna, Bulgaria Irena Valova University of Rousse, Bulgaria Mariana Stoeva Technical University of Varna, Bulgaria Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Abstr Act Image Databases (IDBs) are a kind of Spatial Databases where a large number of images are stored and queried. In this chapter, techniques for indexing an IDB for efciently processing several kinds of queries, like retrieval based on features, content, structure, processing of joins, and queries by example are reviewed. The main indexing techniques used in IDBs are either members of the R-tree family (data driven structures), or members of the quadtree family (space driven structures). Although, research on IDB indexing counts several years, there are still signicant research challenges, which are also discussed in this chapter. IDBs and their indexing structures bring together two different disciplines (databases and image processing) and interdisciplinary research efforts are required. Moreover, deal- ing with the semantic gap (successful integrated retrieval based on low-level features and high-level semantic features) and querying between images and other kinds of spatial data are also signicant future research directions. 21 Image Database Indexing Techniques Introduct Ion Image Databases (IDBs) are a special kind of Spatial Databases where a large number of images are stored and queried. IDBs have a plethora of applications in modern life, for example in medi- cal, multimedia, and educational applications. In the framework of Geographical Information Systems (GIS), digital images (raster data) may represent changes in cultivations, sunny areas, and the discrimination between urban environments and country sides. Apart from the raster format, GIS data may be stored in vector format (points, line segments, polygons, etc.). Each of these data formats has certain advantages making a choice between them a challenge. Raster data leads to faster computing for several operations (e.g., overlays) and are well suited for remote sensing. On the other hand, they have a xed resolution leading to limited detail. In this article, we focus on raster data (image databases) and their indexing techniques. Since the start of the 1980s several structures for spatial objects have been proposed in the lit- erature for efcient storage and retrieval of image collections. Based on these methods, many kinds of useful queries on image data may be processed efciently. These include: • Que ries about the content of additional properties (descriptive information) that have been embedded for each image (e.g., which images have been used in the book cover of children’s books?). • Que ries about the characteristics/features of the images like color, texture, shape etc. (e.g., nd the images that depict vivid blue sky.). • Queries for retrieving images with specied content (e.g., nd the images that contain the sub-image of a specied chair.). • Que ries by example or sketch (e.g., a sample image is chosen, or drawn by the user and images similar to this sample are sought.). • Str uctural queries (e.g., nd the images that contain a number of specic objects in a specied arrangement.). • Ima ge Joins (e.g., nd the cultivation areas that reside in polluted atmosphere areas.). • Queries that combine regional data and other sorts of spatial data (e.g., nd the cities represented by point data that reside within 5km from cotton cultivations.). • Temporal Queries on sequences of evolving images (e.g., nd if there has been an increase in the regions of wheat cultivations in this prefecture during the last two years.). The importance of image indexing and query- ing techniques led major Database Management Systems’ manufacturers to embed related exten- sions to the core engine of their products, (e.g., DB2 has embedded QBIC technology) (Flickner et al. 1995) and Oracle provides Content-Based Image Retrieval (CBIR) based on Virage (An- namalai et al. 2000). bAckground A digital image is a representation of a two-di- mensional image as a nite set of digital values, called picture elements or pixels. In a binary image, each pixel can be either black, or white, while in a greyscale (color) image each pixel cor- responds to a shade of gray (to a color), among a set of permitted greyscale (color) values. Each image represents a scene containing objects and regions. An IDB is an organized collection of digital images aiming at the man- agement and the efcient processing of queries on this image collection. There are numerous publications in the literature related to the pro- cessing of queries on image features like color (e.g., distribution of colors, dominant colors, and color moments), texture (the pattern of the image surface change, usually expressed by a combination of characteristics like coarseness, contrast, directionality, uniformity, regularity, 22 Image Database Indexing Techniques density, frequency, etc.) and shape (the physical structure of objects, or the geometric shapes pres- ent in the image). In several of these publications (emerging from the image processing/computer vision community) the term indexing refers to the features corresponding to each image and to the algorithm used for computing the similarity between them (the algorithm often works by an exhaustive comparison with all the images pres- ent in the databases). In this article, indexing is used in the context of databases and corresponds to the access methods (data structures) used to speed up query processing. Several publications that contain review mate- rial have appeared in the literature. Rui et al. (1999) review numerous papers covering several aspects of CBIR, including multidimensional indexing, and identify open research issues. Smeulders et al. (2000) is another detailed review of CBIR techniques covering the research presented up to the year 2000 that includes also a subsection on storage and indexing. The last section of this paper presents the authors’ view on CBIR’s fu- ture trends. Manolopoulos et al. (2000) overview indexing for structural and feature based queries. Veltkamp and Tanase (2001) performed a survey on numerous CBIR systems providing informa- tion that is available for each of them on several technical aspects, including the use of indexing structures. One conclusion of this survey is that “Indexing data structures are often not used”. Manouvrier et al. (2005) present a detailed review of quadtree based indexing in the image domain ranging from image representation and storage to CBIR. Price (2006) maintains an extensive Computer Vision bibliography (an invaluable tool for the researcher) that contains many references to image indexing. MAIn focus of the Art IcLe In this section, we review the main indexing techniques that have been proposed for image databases. These techniques are grouped and classied by the family of their structure. The two main families are the R-tree family (data-driven structures) and the quadtree family (space-driven structures), (subsection 2.1.2 of chapter 6, Mano- lopoulos et al. 2000). Chang (1987) proposed the use of 2-D strings for the structural representation of objects appear- ing in an image. Using this technique, structural queries can be answered by exhaustive com- parisons with all the images in the IDB. Petrakis (1993) and Orphanoudakis (1996) used hash-based indexing to speed up processing. 2D strings are an efcient representation of the “left/right” and “below/above” relationships. Petrakis and Faloutsos (1997), Petrakis (2002) and Petrakis et al. (2002) adopted Attributed Relational Graphs (ARG), the most general image structure rep- resentation method, where individual object, or regions are represented by graph nodes and their relationships are represented by edges between such nodes. The method developed by Petrakis and Faloutsos (1997) achieves fast query processing by making certain assumptions on the presence of objects in each image. Petrakis (2002) and Petrakis et al. (2002) relax these assumptions. All these ARG-based methods achieve high performance by indexing ARGs with the R-tree family structure. An R-tree is a balanced multiway tree for secondary storage, where each node is related to a Minimum Bounding Rectangle (MBR), that represents the minimum rectangle that bounds the data elements contained in the node. The MBR of the root bounds all the data stored in the tree. Figure 1 depicts some rectangles (MBRs of data e lem ents) on the right and the corresponding R- tree on the left. Dotted lines denote the bound- ing rectangles of the subtrees that are rooted in inner nodes. The most widely used R-tee is the R*-tree; for more details refer to Gaede and Günther (1998). Papadias et al. (1998) treat the problem of structural image queries as a Multiple Constraint 23 Image Database Indexing Techniques Satisfaction (MCS) problem. Both the images and the queries are mapped to regions in a multidi- mensional space and are indexed by structures of the R-tree family. Query processing is treated as general form of spatial joins (multi-way spatial joins). QBIC (Flincker et al. 1995) was one of the rst systems that introduced multidimensional indexing to enhance performance of CBIR. Color, shape and texture features are extracted from the images and are represented by points in high-dimensional spaces. Karhunen Loeve Transform is used to perform dimension reduc- tion of the feature data (in order to overcome the degradation of performance of multidimensional index structures as the dimensionality increases, a situation known as the “curse of dimensional- ity”, Lin et al. 1994) and a structure belonging to the R-tree family (an R*-tree) is used as a multi- dimensional indexing structure. Seidl and Kriegel (2001) present techniques for adaptable similarity search. They use quadratic distance functions that are evaluated using multi- dimensional index structures of the R-tree family (and especially X-trees), dimensionality reduction and approximation techniques (for an introduction to X-trees, see Manolopoulos et. al. 2000). For efcient processing of queries in image databases, Quadtrees have also been extensively used as indexing mechanisms. The Quadtree is a four-way unbalanced tree where each node cor- responds to a subquadrant of the quadrant of its father node (the root corresponds to the whole space). These trees subdivide space in a hierarchi- cal and regular fashion. They are mainly designed for main memory, however several alternatives for secondary memory have been proposed. The most widely used Quadtree is the Region Quadtree that stores regional data in the form of raster images. Figure 2 depicts an 8x8 pixel array and the corre- sponding Quadtree. Note that black/white squares represent black/white regions, while circles rep- resent internal nodes (gray regions). The Linear Region Quadtree is an external memory version of the Region Quadtree, where each quadrant is represented by a codeword stored in a B+-tree; for more details refer to Samet (1990). Quadtrees have been used for CBIR (represen- tation and querying by image features) by several researchers, as a mechanism for calculating im- age similarity by dening appropriate similarity measures. Examples of such work follows. In some research efforts, complete Quadtrees with a xed number of levels are used, since they lead to precisely enough results. Each node in the Quadtree stores the features that correspond to its quadrant, for example, a color histogram (Lin et al., 2001), or a combination of feature Figure 1. An example of an R-tree A B C D E G F H I K J L M N G F E D K J H N M L A B I C Figure 2. An example of a Region Quadtree 24 Image Database Indexing Techniques histograms (Malki et al, 1999). De Natale and Granelli (2001) use unbalanced Quadtrees for image segmentation to dominant colors. Each quadtree is modelled by a binary array represent- ing its structure and a label array representing the dominant color associated to each node or leaf. Ahmad & Grosky (2003) use unbalanced Quadtrees to decompose an image into a spatial arrangement of features points (extracted using image processing techniques) and to quantify image similarity, while providing geometric vari- ance independence. For search and retrieval, an indexing scheme based on image signatures and quadtrees is used. Chakrabarti et al. (2000), use Quadtrees to represent two-dimensional shapes and perform shape-based similarity retrieval. The proposed representation is designed to exhibit invariance to scale, translation and rotation. Overlapping has been applied to Linear Region Quadtrees (Tzouramanis et al. 2004). In this and previous papers by the same authors, four differ- ent extensions of the Linear Region Quadtree are presented for indexing a sequence of evolving raster data. Moreover, temporal window queries are dened and studied. These queries relate to the evolution of regional data inside a window in the course of time. Quadtrees have also been used for creating an IDB, where image retrieval, insertion, deletion, comparison and set operations can be applied. A single quadtree is used for all images. Its nodes are associated with the list of images that have information in the respective quadrants. Vas- silakopoulos & Manolopoulos (1995) proposed Dynamic Inverted Quadtrees, while Jomier et al. (2000) proposed a version suitable for binary, gray scale or color images. Corral et al. (1999) combine two different kinds of data and two different kinds of indexing structures. They present ve algorithms suitable for processing join queries between point data stored in an R-tree and image data stored in a Linear Region Quadtree. Due to space limitations, the most prominent IDB indexing structures are reviewed in this article. The choice of an indexing method among them depends on the application. Each of the above techniques has been designed around a specic problem setting. A qualitative comparison between them is an interesting direction for future work that lies beyond the scope and the size limit of this article. Descriptions of several other Spatial Access Methods that have been used in IDBs can be found in Samet (1990), Gaede and Günther (1998) and Manolopoulos et. al. (2000). future trends IDBs are related to two different scientic commu- nities: database and image processing / computer vision researchers. Multidimensional access meth- ods as well as information retrieval techniques and their use for query processing, constitute the key meeting point of the two worlds. Several of the techniques of the image processing community could make further use of access methods or/and adapt to their properties, leading to more efcient processing of image related queries. Related to the previous research direction is the further develop- ment of systems able to retrieve (and, in general, process queries) from image collections existing in different sources, including the WWW (Rui et al. 1999) and indexing techniques are expected to play a dominant role in them. In Zhao and Grosky (2002) one of the rst techniques for integrated image retrieval based on low-level features and high-level semantic features of images is presented. Mojsilovic et al. (2004) present a methodology for semantic-based image retrieval based on low-level image descrip- tors. However, neither of these works is based on indexing structures. Since image retrieval based on both these kinds is features is crucial for the usefulness of CBIR systems (for a discussion of the semantic gap see subsection 2.4 of, Smeulders et al. 2000) and still remains one of the big chal- 25 Image Database Indexing Techniques lenges for researchers, indexing structures could be used in this context for calculating the correla- tions between low-level features and high-level concepts efciently. In Mao et al. (2005) distance-based tree struc- tures are used for computing the similarity of im- ages, which are represented by features reecting their structure, texture and color. Although the high dimensionality of the feature space sug- gests that distance-based indexing techniques are outperformed by sequential scan (curse of dimensionality), the authors show that the in- trinsic dimensionality of real data is low and can apply distance-based indexing that is specically designed to reect the intrinsic clustering of real data. The design and study of more generalized techniques in this direction is another research challenge. Despite the extensive research performed in spatial / spatio-temporal databases, storing a large database of (possibly evolving) images, or of regional data sets and being able to efciently answer queries between these data and other sorts of spatial/spatiotemporal data, or queries involving the notion of time is still a big research challenge. For example, being able to efciently answer queries like: nd the boats (moving points) that were inside the storm (changing regional data) during this morning (a time interval). conc Lus Ion In this paper, we have reviewed techniques related to indexing an image database as a means for ef- ciently processing several kinds of queries, like retrieval based on features, content, structure, processing of joins, and queries by example. Although, research in this scientic area counts several years, there are still signicant research challenges. Image databases and their indexing structures bring together two different disciplines (databases and image processing) and develop- ing a true Image Database System requires interdisciplinary research efforts. Nevertheless, the semantic gap is alive and querying between images and other kinds of spatial data has not attracted enough attention yet. r eferences Ahmad, I., & Grosky, W. I. (2003). Indexing and retrieval of images by spatial constraints. Journal of Visual Communication and Image Representa- tion, 14(3), 291-320. Annamalai, M., Chopra, R., DeFazio, S., & Ma- vris, S. (2000). Indexing images in oracle8i. In Proc. SIGMOD’00, 539-547. Chakrabarti, K., Ortega-Binderberger, M., Por- kaew, K., Zuo, P., & Mehrotra, S. (2000). Similar Shape Retrieval in MARS. In Proc. IEEE Int. Conf. on Multimedia and Expo (II), 709-712. Chang, S. K., Shi, Q. Y., & Yan, C. W. (1987). Iconic indexing by 2-d strings. IEEE Trans. Pat- tern Anal. Machine Intell., 9, 413-427. Corral, A., Vassilakopoulos, M., & Manolopoulos, Y. (1999). Algorithms for Joining R-trees and Linear Region Quadtrees. In Proc. of SSD’99, LNCS 1651, 251-269. Spinger Verlag. De Natale, F. G. B., & Granelli, F. (2001). Struc- tured-Based Image Retrieval Using a Structured Color Descriptor. In Proc. Int. Workshop on Content-Based Multimedia Indexing (CBMI’01), 109-115 Flickner, M., Sawhney, H., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Pet- kovic, D., Steele, D., & Yanker, P. (1995). Query by Image and Video Content: The QBIC System. IEEE Computer 28(9), 23-32 Gaede, V., & Günther, O. (1998). Multidimensional Access Methods. ACM Computing Surveys, 30(2), 170-231. [...]... Comparisons of the dimensionality and features of simulated Landsat-4 MSS and TM data Remote Sensing of Environment, 14( 1-3 ), 23 5 -2 46 Crist, E P., & Cicone, R C (1984b) A physically-based transformation of Thematic Mapper data-the TM tasseled cap IEEE Transactions on Geoscience and Remote Sensing, 22 (3), 25 626 3 Cockcroft, S (1997) A Taxonomy of Spatial Data Integrity Constraints GeoInformatica 1(4), 32 7-3 43... Neural Networks, 2( 5), 35 9-3 66 Hruschka, H., & Natter, M (1999) Comparing Performance of Feedforward Neural Nets and Kmeans for Market Segmentation European Journal of Operational Research, 114 (2) , 34 6-3 53 Hunter, A., & Liu, W (20 05) Fusion rules for merging uncertain information Information Fusion, 7, 9 7-1 34 Hunter, A., & Summerton, R (20 04) Fusion rules for context-dependent aggregation of structured... effectiveness of EIA and SEA studies Environmental Impact Assessment Review, 25 (2) , 12 3-1 42 Wald, L (20 00) A Conceptual Approach To The Fusion Of Earth Observation Data Surveys in Geophysics, 2( 2- 3 ), 17 7-1 86 Wald, L (1999) Some Terms of Reference in Data Fusion IEEE Transactions on Geosciences and Remote Sensing, 37(3), 119 0-1 193 World Wide Web Consortium (20 00), Extensible Markup Language (XML) 1.0, 2nd ed.,... fusion Data fusion is the process of de- 32 tection, association, correlation, and combination of data and information from multiple sources In order to lead the reader further, material mentioned in the list of references are suggested and allow one to go beyond the traditional transactional data fusion capabilities R eferences Anandarajan, M (20 02) Profiling Web Usage in the Workplace: A Behavior-based... meta-level information about the provenance of the information used and the mode of aggregation (Hunter and Liu 20 05; Hunter and Summerton 20 04) Spatial data fusion is a combination of the above mentioned with the dimension of spatiality It is by definition an enormous and complex field, comprising issues ranging from registration and pixel-level fusion of data for improving the spatial resolution of managerial... Genova: Offie for Official Publication of the European Communities Lemmens, R., Granell, C., Wytzysk, A., By, R d., Gould, M., & Oosterom, P v (20 06, 2 0 -2 2 April, 20 06) Semantic and Syntactic Service Descriptions at Work in Geo-Service Chaining Paper presented at the 9th AGILE Conference on Geographic Information Science, Visegrád, Hungary Lutz, M., & Klien, E (20 06) Ontology-Based Retrieval of Geographic... M (20 00) Storage and Management of Similar Images Journal of the Brazilian Computer Society (JBCS), 3(6), 1 3 -2 6 Petrakis, E., & Orphanoudakis, S (1993) Methodology for the representation, indexing and retrieval of images by content Image Vision Comput 11(8), 50 4-5 21 Lin, K.-I., Jagadish, H V., & Faloutsos, C (1994) The TV-tree - an index structure for high-dimensional data VLDB Journal, 3, 51 7-5 42 Petrakis,... regions An early (1980s) European exercise in cross-border harmonization, stitching together nationally-produced pieces of the Coordinated Information on the European Environment (CORINE) land cover database, highlighted some of these discrepancies at regional and national borders: experts on both sides disagreed on how to classify the same, cross-border land cover regions SD I essent ial co mponents... Indexing and Retrieval Based οn 2- D String In Jungert C & Tortora G (eds.) Intelligent Image Database Systems ( important contributions in the field of spatial projections), World Scientific Publishing Co 19 7 -2 18.  Lin, S., Tamer, Özsu, M., Oria, V., & Ng, R (20 01) An Extendible Hash for Multi-Precision Similarity Querying of Image Databases In Proc of VLDB 20 01, 22 1 -2 30 Malki, J., Boujemaa, N., Nastar,... Geographic Information International Journal of Gegraphical Information Science, 20 (3), 23 3 -2 60 MacEachren, A., Cai, G., Rauschert, I., Brewer, I., Bolelli, I., Shaparenko, B., et al (20 05) Enabling collaborative geoinformation access and decisionmaking through a natural, multimodal interface International Journal of Gegraphical Information Science, 19(Number 3), 29 3-3 17 MacEachren, A M (20 01) Cartography . et al. 1995) and Oracle provides Content-Based Image Retrieval (CBIR) based on Virage (An- namalai et al. 20 00). bAckground A digital image is a representation of a two-di- mensional image as. suggested and allow one to go beyond the traditional transactional data fusion capabilities. r eferences Anandarajan, M. (20 02) . Proling Web Usage in the Workplace: A Behavior-based Articial Intelligence. (Saad et al. 1998), market segmentations (Hruschka & Natter 1999) and Web usage proling (Ananda- rajan 20 02) . It is the universal approximation property of ANNs that makes ANNs one of