Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/~arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible object-oriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability

Apers, P.M.G.

Blanken, H.M.

Doorn, M.G.L.M. van

Vries, A.P. de

English

de Vries, A.P.

van Doorn, M.G.L.M.

Blanken, Henk

Apers, Peter M.G.

NARCIS 

The Mirror MMDBMS architecture

University of Twente Research Information

The Mirror MMDBMS architectureArjen P. de Vries Mark G.L.M. van Doorn Henk M. Blanken Peter M.G. ApersCentre for Telematics and Information TechnologyUniversity of TwenteThe Netherlands1 IntroductionHandling large collections of digitized multimediadata, usually referred to as multimedia digital li-braries, is a major challenge for information tech-nology. The Mirror DBMS is a research databasesystem that is developed to better understand thekind of data management that is required in the con-text of multimedia digital libraries (see also URLhttp://www.cs.utwente.nl/˜arjen/mmdb.html). Its main fea-tures are an integrated approach to both content man-agement and (traditional) structured data manage-ment, and the implementation of an extensible object-oriented logical data model on a binary relational phys-ical data model. The focus of this work is aimed atdesign for scalability.2 Query processingThe query facilities of the Mirror DBMS rely on theMoa Object Algebra [BWK98]. Moa constitutesan object data model and query algebra, designed tobe used at the logical level of a DBMS. It has beenimplemented on top of the Monet extensible databasemanagement system. The Moa data model is based onthe principle of ‘structural object-orientation’. Struc-tures, such as tuple and (multi-)set, define complexdata types out of the simple base types. The basetypes, such as integer and string, are inherited fromthe underlying physical database. This way, Moa in-troduces the notion of data independence into theworld of object-oriented databases: the translationfrom the logical data model into a different physi-cal model (Monet supports a binary relational datamodel) provides an excellent basis for algebraic queryPermission to copy without fee all or part of this material isgranted provided that the copies are not made or distributed fordirect commercial advantage, the VLDB copyright notice andthe title of the publication and its date appear, and notice isgiven that copying is by permission of the Very Large Data BaseEndowment. To copy otherwise, or to republish, requires a feeand/or special permission from the Endowment.Proceedings of the 25th VLDB Conference,Edinburgh, Scotland, 1999.optimization, and allows often for set-at-a-time pro-cessing of complex query expressions.The Moa kernel provides the tuple (or record) struc-ture, and the multi-set structure. The resulting datamodel is equivalent to what is generally known as theNF2 algebra. However, Moa object algebra is morethan ‘just’ an implementation of NF2 algebra. It isan open complex object system, supporting extensibil-ity of structures. Thus, new structures can be addedto the system, similar to the well-known principle ofbase type extensibility in object-relational databasesystems. Obviously, this enables the definition of newgeneric structures, such as lists. A more interesting usefor structural extensibility is however the definition ofdomain specific structures.3 Content managementThe prototype implementation of our database systemdemonstrates the particular application of Moa’s ex-tensibility in the domain of multimedia informationretrieval. An information retrieval (IR) model con-sists of three parts: a document and query represen-tation scheme, a ranking function which determinesto which extent a document is relevant to a query,and a query formulation model [WY95]. Documentsand queries are usually represented by its terms. TheCONTREP Moa structure supports the ranking schemeknown as the inference network retrieval model. Thisretrieval model is the basis of the successful IR systemInQuery. It allows flexible modeling of the combina-tion of evidence originating from different sources. Wehave adapted this text retrieval model to handle alsomultimedia information retrieval [dV98]. New struc-tures in Moa, supported by new probabilistic operatorsat the physical level, provide an efficient implementa-tion of the inference network retrieval model.To illustrate the use of these extensions, we modela traditional digital library of (manually) annotatedimages using these Moa structures. Assume that animage is identified by its URL, and its textual anno-tation is indexed using the inference network retrievalmodel:define TraditionalImgLib as758SET<TUPLE<Atomic<URL>: source,CONTREP<Text>: annotation>>;Ranking the images with respect to a query is thenperformed with the following query, in which queryrefers to a set of query terms, and stats is a structurethat represents global statistics of the whole collec-tion:1map[sum(THIS)](map[getBL(THIS.annotation,query, stats)]( TraditionalImgLib ));Because these query expressions can be combinedwith ‘normal’ relational operators (such as select orjoin), the resulting system is an efficient integration ofinformation and data retrieval. This way, it is possibleto refer to both structure and content of multimediadata in a single query (see also [dVW99]).4 Distributed architectureClient(s)Daemon(s)Multimedia Content Server(s) Meta-data DatabaseDistributed Data DictionaryFigure 1: The distributed architectureAnother aspect of our design is related to more prac-tical issues for the creation and maintenance of a multi-media digital library. A digital library involves severalmore or less independent parties, including human an-notators, software to extract meta data automatically,and owners of multimedia footage [dVEK98]. Hence,we believe that a digital library can only be a successif it follows the model of the web. We use an open dis-tributed architecture instead of a monolithic databasesystem, cf. figure 1 [dVB98]. The notion of a ‘daemon’abstracts from the various techniques for meta dataextraction and query formulation. Using CORBA, weallow distribution of operations, establishing indepen-dence between the management of meta data and theparties that create these meta data.5 The demo systemIn the Mirror architecture, the retrieval application isnot integrated in the database system itself, unlike in1For further details about the query language and the struc-tural extensions, refer to [dV98] and [dVW99]most ‘multimedia databases’ found in literature. TheMirror DBMS provides the basic functionality for prob-abilistic inference, multimedia data types, and featureextraction techniques, just like traditional databasesystems provide the basic functionality to build ad-ministrative applications. In this section, we describean example image retrieval application, using the func-tionality provided by the Mirror DBMS. The underly-ing philosophy of this application has been inspiredby theories from cognitive psychology, in particularPaivio’s dual coding theory [EK95]. Aspects of its de-sign are similar in spirit to both the Viper [SMMR99]and the FourEyes [MP97] image retrieval systems.5.1 Our prototype environmentThe digital library constructed for the demo consistsof images collected by a simple web robot. Someof the images in the library are annotated withtext. One of the daemons segments the images.Several feature extraction daemons independentlycreate feature representations of the image segments.At the moment of writing, we have implementedtwo color histogram daemons. In addition, we usethe four reference implementations of texture algo-rithms provided by the MeasTex framework (see URLhttp://www.cssip.elec.uq.edu.au/˜guy/meastex/meastex.html).These feature spaces are then clustered using thepublic domain clustering package AutoClass [CS95].Furthermore, we have thesaurus daemons that areinteractively used during query formulation. TheMirror DBMS implements the meta data database (seefigure 1), which contains the content representations.The media server is a web server.5.2 The example applicationThe data model of the image library can be specifiedby the application programmer as:define ImageLibrary asSET<TUPLE<Atomic<URL>: source,Atomic<Text>: annotation,Atomic<Image>: image>>;Next, the daemons in the prototype environmentstart to work on this schema. Like the examplein section 3, the text annotations are indexed andrepresented as CONTREP structures. The images aresegmented, and feature representations are extractedfrom the segments, creating the following (internal)intermediate schema:SET<TUPLE<Atomic<URL>: source,CONTREP<Text>: annotation,759SET<TUPLE<Atomic< Image >: segment,Atomic< Vector >: RGB,Atomic< Vector >: Gabor,...>>: image_segments>>;The feature spaces are clustered with AutoClass, toobtain a representation of the image content that canbe queried using the CONTREP structure. We furtheruse the identified clusters as if they are words in textretrieval; they become the basic blocks of ‘meaning’for multimedia information retrieval. This results inthe following internal schema, which corresponds tothe original ImageLibrary schema:define ImageLibraryInternal asSET<TUPLE<Atomic<URL>: source,CONTREP<Text>: annotation,CONTREP<Image>: image>>;Of course, the clusters in the image content repre-sentation (such as ‘gabor 21’) are not suited for inter-action with the users of the digital library. Therefore,we automatically construct a thesaurus, associatingwords in the textual annotations to the clusters in theimage content representation. An interesting aspect ofthis approach, is that this thesaurus can be consideredan implementation of Paivio’s dual coding theory. Fol-lowing the observation used in PhraseFinder [JC94],an association thesaurus can be seen as measuring thebelief in a concept (instead of in a document) giventhe query. Thus, the domain specific Moa structuresthat model IR query evaluation, can also be used forquery formulation using the thesaurus.Querying the digital image library now takes placeas follows. First, the user enters an initial (usuallytextual) query. Next, we use the thesaurus to selectclusters from the image content representations thatare relevant to this initial query. Assuming that theresult is a Moa expression called query, we then re-trieve images from the digital library as follows:map[sum(THIS)](map[getBL(THIS.image,query, stats)]( ImageLibraryInternal ));The results of this query are shown to the user. Theuser may provide relevance feedback for these images;this relevance feedback is used to improve the currentquery. A problem for the current retrieval system isthat the thesaurus sometimes associates words in theannotations to irrelevant clusters, or AutoClass identi-fies clusters of little semantic value. To alleviate theseproblems, we are investigating machine learning tech-niques to adapt the thesaurus and the content repre-sentation, using the relevance feedback across querysessions.As we demonstrated, the multimedia querying pro-cess is expressed in Moa expressions. The MirrorDBMS provides the primitives for managing the im-ages and its meta data, as well as the probabilistic in-ference required during the interaction with the user.New feature models, different clustering algorithms,or different query modification techniques, can easilybe added or modified. Hence, a variety of multime-dia retrieval systems can be implemented by simplychanging the sequence of Moa expressions issued inthe Mirror DBMS.6 AcknowledgmentsThis demo could never have been created without theMoa kernel, designed and implemented mainly by An-nita N. Wilschut and Jan Flokstra. Erik van het Hofhas implemented the open distributed architecture offigure 1. Both Henk Ernst Blok, who added the LISTstructure to Moa, and Roelof van Zwol, contributedsignificantly in discussions about the system design.Finally, we want to thank the Monet team, and PeterBoncz in particular, for the support, explanations, andall those nightly hours spent tracing bugs and fixingthem.References[BWK98] P. Boncz, A.N. Wilschut, and M.L. Ker-sten. Flattening an object algebra to pro-vide performance. In Fourteenth Inter-national Conference on Data Engineering,pages 568–577, Orlando, Florida, Febru-ary 1998.[CS95] P. Cheeseman and J. Stutz. Bayesianclassification (AutoClass): Theory and re-sults. In Advances in Knowledge Discoveryand Data Mining. AAAI Press, 1995.[dV98] A.P. de Vries. Mirror: Multimedia queryprocessing in extensible databases. In Pro-ceedings of the fourteenth Twente work-shop on language technology (TWLT14):Language Technology in Multimedia Infor-mation Retrieval, pages 37–48, Enschede,The Netherlands, December 1998.[dVB98] A.P. de Vries and H.M. Blanken. Databasetechnology and the management of mul-timedia data in Mirror. In MultimediaStorage and Archiving Systems III, volume3527 of Proceedings of SPIE, pages 443–455, Boston MA, November 1998.760[dVEK98] A.P. de Vries, B. Eberman, and D.E. Ko-valcin. The design and implementation ofan infrastructure for multimedia digital li-braries. In Proceedings of the 1998 Inter-national Database Engineering & Applica-tions Symposium, pages 103–110, Cardiff,UK, July 1998.[dVW99] A.P. de Vries and A.N. Wilschut. Onthe integration of IR and databases. InDatabase issues in multimedia; short pa-per proceedings, international conferenceon database semantics (DS-8), Rotorua,New Zealand, January 1999.[EK95] M.W. Eysenck and M.T. Keane. CognitivePsychology. A student’s handbook, chapterMental representation. Lawrence ErlbaumAssociates, 3 edition, 1995.[JC94] Y. Jing and W.B. Croft. An associationthesaurus for information retrieval. Tech-nical Report 94-17, University of Mas-sachusetts, 1994.[MP97] T.P. Minka and R.W. Picard. Interactivelearning using a “society of models”. Pat-tern Recognition, 30(4), 1997.[SMMR99] D. McG. Squire, W. Müller, H. Müller,and J. Raki. Content-based query of imagedatabases, inspirations from text retrieval:inverted files, frequency-based weights andrelevance feedback. In The 11th Scan-dinavian Conference on Image Analysis,Kangerlussuaq, Greenland, June 1999. Toappear.[WY95] S.K.M. Wong and Y.Y. Yao. On modelinginformation retrieval with probabilistic in-ference. ACM Transactions on Informa-tion Systems, 13(1):38–68, January 1995.761

Vries, Arjen

Doorn, Mark

Apers, Peter

The mirror MMDBMS architecture

Vries, A.P. (Arjen) de

Doorn, M.G.L.M. (Mark) van

Apers, P.M.G. (Peter)

CWI's Institutional Repository

The Mirror MMDBMS architecture Arjen P. de Vries J\lark G.L.M. van Doorn Henk :tvl. Blanken Peter M.G. Apers Centre for Telematics and Information Technology University of Twente The Netherlands 1 Introduction Handling large collections of digitized multimedia data, usually referred to as multimedia digital li-brarif:'s, is a major challenge for information tech-nology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the con-text of multimedia digital libraries (see also URL http://www.cs.utwente.nt;-arjen/mmdb.html ). Its main fea-tures are an integrated approach to both content man-agement and (traditional) structured data manage-ment, and the implementation of an extensible object-oriented logical data model on a binary relational phys-ical data model. The focus of this work is aimed at design for scalability. 2 Query processing The query facilities of the Mirror DBMS rely on the Moa Object Algebra [BWK98]. Moa constitutes an object data model and query algebra, designed to be used at the logical level of a DBMS. It has been implemented on top of the Monet extensible database management system. The hfoa data model is based on the principle of 'structural object-orientation'. Struc-tures, such as tuple and (multi-)set, define complex data types out of the simple base types. The base types, such as integer and string, are inherited from the underlying physical database. This way, Moa in-troduces the notion of data independence into the world of object-oriented databases: the translation from the logical data model into a different physi-cal model (Monet supports a binary relational data model) provides an excellent basis for algebraic query PermisS"ion to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and not-ice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, req·uires a fee and/or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. 758 optimization, and allows often for set-at-a-time pro-cessing of complex query expressions. The Moa kernel provides the tuple (or record) struc-ture and the multi-set structure. The resulting data mod,el is equivalent to what is generally known as the NF2 algebra. However, Moa object algebra is more than 'just' an implementation of NF2 algebra. It is an open complex object system, supporting extensibil-ity of structures. Thus, new structures can be added to the system, similar to the well-known principle of base type extensibility in object-relational database systems. Obviously, this enables the definition of new generic structures, such as lists. A more interesting use for structural extensibility is however the definition of domain specific structures. 3 Content management The prototype implementation of our database system demonstrates the particular application of Moa's ex-tensibility in the domain of multimedia information retrieval. An information retrieval (IR) model con-sists of three parts: a document and query represen-tation scheme, a ranking function which determines to which extent a document is relevant to a query, and a query formulation model [WY95]. Documents and queries are usually represented by its terms. The CONTREP Moa structure supports the ranking scheme known as the inference network retrieval model. This retrieval model is the basis of the successful IR system InQuery. It allows flexible modeling of the combina-tion of evidence originating from different sources. We have adapted this text retrieval model to handle also multimedia information retrieval [dV98]. New struc-tures in Moa, supported by new probabilistic operators at the physical level, provide an efficient implementa-tion of the inference network retrieval model. To illustrate the use of these extensions, we model a traditional digital library of (manually) annotated images using these Moa structures. Assume that an image is identified by its URL, and its textual anno-tation is indexed using the inference network retrieval model: define TraditionalimgLib as SET< TUPLE< Atomic<URL>: source, CONTREP<Text>: annotation »; Ranking the images with respect to a query is then performed with the following query, in which query refers to a set of query terms, and stats is a structure that represents global statistics of the whole collec-tion: 1 map[sum(THIS)] ( map[getBL(THIS.annotation, query, stats)] ( TraditionalimgLib )); Because these query expressions can be combined with 'normal' relational operators (such as select or join), the resulting system is an efficient integration of information and data retrieval. This way, it is possible to refer to both structure and content of multimedia data in a single query (see also [dVW99]). 4 Distributed architecture IB ~ ~j n1~1nb11.tcJ [lat&1fhction11.ry l'Ja1:m1.1n{~) t'hcnU.s) Figure 1: The distributed architecture Another aspect of our design is related to more prac-tical issues for the creation and maintenance of a multi-media digital library. A digital library involves several more or less independent parties, including human an-notators, software to extract meta data automatically, and owners of multimedia footage [dVEK98]. Hence, we believe that a digital library can only be a success if it follows the model of the web. We use an open dis-tributed architecture instead of a monolithic database system, cf. figure 1 [dVB98]. The notion of a 'daemon' abstracts from the various techniques for meta data extraction and query formulation. Using CORBA, we allow distribution of operations, establishing indepen-dence between the management of meta data and the parties that create these meta data. 5 The demo system In the Mirror architecture, the retrieval application is not integrated in the database system itself, unlike in 1 For further details about the query language and the struc-tural extensions, refer to [dV98] and [dVW99) 759 most 'multimedia databases' found in literature. The Mirror DBMS provides the basic functionality for prob-abilistic inference, multimedia data types, and feature extraction techniques, just like traditional database systems provide the basic functionality to build ad-ministrative applications. In this section, we describe an example image retrieval application, using the func-tionality provided by the Mirror DBMS. The underly-ing philosophy of this application has been inspired by theories from cognitive psychology, in particular Paivio's dual coding theory [EK95]. Aspects of its de-sign are similar in spirit to both the Viper [SMMR99] and the FourEyes (MP97] image retrieval systems. 5.1 Our prototype environment The digital library constructed for the demo consists of images collected by a simple web robot. Some of the images in the library are annotated with text. One of the daemons segments the images. Several feature extraction daemons independently create feature representations of the image segments. At the moment of writing, we have implemented two color histogram daemons. In addition, we use the four reference implementations of texture algo-rithms provided by the MeasTex framework (see URL http://www.cssip.elec.uq.edu.au;-guy /mea.stex/meastex.html ). These feature spaces are then clustered using the public domain clustering package AutoClass [CS95]. Furthermore, we have thesaurus daemons that are interactively used during query formulation. The Mirror DBMS implements the meta data database (see figure 1), which contains the content representations. The media server is a web server. 5.2 The example application The data model of the image library can be specified by the application programmer as: define ImageLibrary as SET< TUPLE< >>; Atomic<URL>: source, Atomic<Text>: annotation, Atomic<Image>: image Next, the daemons in the prototype environment start to work on this schema. Like the example in section 3, the text annotations are indexed and represented as CONTREP structures. The images are segmented, and feature representations are extracted from the segments, creating the following (internal) intermediate schema: SET< TUPLE< Atomic<URL>: source, CONTREP<Text>: annotation, >>; SET< TUPLE< > Atomic< Image >: segment, Atomic< Vector >: RGB, Atomic< Vector>: Gabor, >: image_segments The feature spaces are clustered with AutoClass, to obtain a representation of the image content that can be queried using the CONTREP structure. We further use the identified clusters as if they are words in text retrieval; they become the basic blocks of 'meaning' for multimedia information retrieval. This results in the following internal schema, which corresponds to the original ImageLibrary schema: define ImageLibraryinternal as SET< TUPLE< >>; Atomic<URL>: source, CONTREP<Text>: annotation, CONTREP<Image>: image Of course, the clusters in the image content repre-sentation (such as 'gabor_21 ') are not suited for inter-action with the users of the digital library. Therefore, we automatically construct a thesaurus, associating words in the textual annotations to the clusters in the image content representation. An interesting aspect of this approach, is that this thesaurus can be considered an implementation of Paivio's dual coding theory. Fol-lowing the observation used in PhraseFinder [JC~4], an association thesaurus can be seen as measuring the belief in a concept (instead of in a document) given the query. Thus, the domain specific l\loa structures that model IR query evaluation, can also be used for query formulation using the thesaurus. Querying the digital image library now takes place as follows. First, the user enters an initial (usually textual) query. Next, we use the thesaurus to select clusters from the image content representations that arc relevant to this initial query. Assuming tlrnt the result is a Moa expression called query, we then re-trieve images from the digital library as follows: map [sum (THIS)] ( map[getBL(THIS.image, query, stats)] ( ImageLibraryinternal )) ; The results of this query are shown to the user. The user may provide relevance feedback for these images; this relevance feedback is used to improve the current query. A problem for the current retrieval system is that the thesaurus sometimes associates words in the 760 annotations to irrelevant clusters, or AutoClass identi-fies clusters of little semantic value. To alleviate these problems, we are investigating machine learning tech-niques to adapt the thesaurus and the content repre-sentation, using the relevance feedback across query sessions. As we demonstrated, the multimedia querying pro-cess is expressed in Moa expressions. The Mirror DBMS provides the primitives for managing the im-ages and its meta data, as well as the probabilistic in-ference required during the interaction with the user. New feature models, different clustering algorithms, or different query modification techniques, can easily be added or modified. Hence, a variety of multime-dia retrieval systems can be implemented by simply changing the sequence of ~foa expressions issued in the Mirror DBMS. 6 Acknowledgments This demo could never have been created without the Moa kernel, designed and implemented mainly by An-nita N. vVilschut and Jan Flokstra. Erik van het Hof has implemented the open distributed architecture of figure l. Both Henk Ernst Blok, who added the LIST structure to Moa, and Roelof van Zwol, contributed significantly in discussions about the system design. Finally, we want to thank the Monet team, and Peter Boncz in particular, for the support, explanations, and all those nightly hours spent tracing bugs and fixing them. References [BWK08] P. Boncz, A.N. Wilschut, and f\I.L. Ker-sten. Flattening an object algebra to pro-vide performance. In Fourteenth Inter-national Conference on Data Engineering, pages 568-577, Orlaudo, Florida, Febru-ary 1998. [CS95] P. Cheeseman and J. Stutz. Bayesian classification ( AutoClass): Theory and re-sults. In Ad,vances -in Knowledge Discovery and Data Mining. AAAI Press, 1995. [dV98] A.P. de Vries. l\Iirror: ;\lultimedia query processing in extensible databases. In Pro-ceedings of the four·teenth Twente work-shop on language technology (TWLT14): [dVB98] Language Technology in Multimedia Infor-mation Retrieval, pages :w 48, Enschede, The Netherlands, December 1098. A.P. de Vries and H.M. Blanken. Database technology and the management of mul-timedia data in Mirror. In M·ultimedia Storage and Archiving Systems III, volume 3527 of Prnceedings of SPIE, pages 443-455, Boston MA, November 1998. [dVEK98] A.P. de Vries, B. Eberman, and D.E. Ko-valcin. The design and implementation of an infrastructure for multimedia digital li-braries. In Proceedings of the 1 gg8 Inter-national Database Engineering & Applica-tions Symposium, pages 103-110, Cardiff, UK, July 1998. [dVW99] A.P. de Vries and A.N. Wilschut. On the integration of IR and databases. In Database issues in multimedia; short pa-per proceedings, international conference on database semantics (DS-8), Rotorua, New Zealand, January 1999. [EK95] M.W. Eysenck and M.T. Keane. Cognitive Psychology. A st,udent 's handbook, chapter Mental representation. Lawrence Erlbaum Associates, 3 edition, 1995. [JC94] Y. Jing and W.B. Croft. An association thesaurus for information retrieval. Tech-nical Report 94-17, University of Mas-sachusetts, 1994. [MP97] T.P. Minka and R.W. Picard. Interactive learning using a "society of models". Pat-tern Recognition, 30(4), 1997. [SMMR99] D. McG. Squire, W. Muller, H. Muller, and J. Raki. Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback. In The 1 lth Scan-dinav'ian Conference on Image Analysis, Kangerlussuaq, Greenland, June 1999. To appear. [WY95] S.K.M. Wong and Y.Y. Yao. On modeling information retrieval with probabilistic in-ference. ACM Transactions on Informa-tion Systems, 13(1):38-68, January 1995. 761 

An association thesaurus for information retrieval.

Bayesian classi (AutoClass): Theory and results.

Cognitive Psychology. A student's handbook, chapter Mental representation. Lawrence Erlbaum Associates, 3 edition,

Content-based query of image databases, inspirations from text retrieval: inverted  frequency-based weights and relevance feedback.

Database technology and the management of multimedia data in Mirror. In Multimedia Storage and Archiving Systems III,

The Mirror MMDBMS architecture

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