CIKM WORKSHOP REPORTThe Second ACM International Workshop
on Multimedia Databases (MMDB 2004)
held at ACM CIKM 2004
Shu-Ching Chen
School of Computer ScienceFlorida International University
Miami, FL 33199, USA
chens@cs.fiu.edu

Mei-Ling Shyu
Department of Electrical & Computer Engineering
University of Miami
Coral Gables, FL 33124, USA
shyu@miami.edu

The Second ACM International Workshop on Multimedia Databases was held in Washington, DC, USA,
November 13, 2004. Its aim is to bring together university researchers, scientists, industry professionals,
software engineers and graduate students who need to become acquainted with new theories andtechnologies in multimedia databases, and to all those who wish to gain a detailed technical understandingof what multimedia databases involve.
We received thirty-one submissions. Each of them was reviewed by at least three referees. The programcommittee, which was constituted of researchers and practitioners from the multimedia database researchareas, selected twelve papers to be presented at the workshop. The authors were asked to address eachand every comment made by the referees to improve the quality of the papers. These papers reflectcurrent trends in the multimedia database research areas with topics such as Content-Based Retrieval for
Multimedia Databases, Multimedia Database Query Processing and Retrieval, Multimedia Data Indexing,
and Multimedia Data Mining. From these twelve papers, the workshop offered three sessions forparticipants representing a variety of backgrounds and perspectives on multimedia databases.
In “Content-Based Sub-Image Retrieval Using Relevance Feedback,” Jie Luo and Mario A. Nascimento
presented their approach that uses relevance feedback in content-based sub-image retrieval (CBsIR) toimprove the accuracy of successive retrievals. A tile re-weighting scheme assigns penalties to each tile ofthe database images and updates the tile penalties for all relevant images retrieved at each iteration using
both the relevant (positive) and irrelevant (negative) images identified by the user. They evaluated theperformance on a dataset of over 10,000 images by using 64 quantized colors in the RGB color space,
their system can achieve a stable average recall value of 70% within the top 20 retrieved (and presented)
images after only 5 iterations, where each iteration was about 2 seconds.
Liang-Tien Chia presented on behalf of Haoran Yi and Deepu Rajan on “A Motion based Scene Tree forBrowsing and Retrieval of Compressed Videos.” In this work, a fully automatic content-based approachfor browsing and retrieval of MPEG-2 compressed video was proposed. The first step of their approach isshot boundary detection based on motion vectors available from the compressed video stream. The second
step is to build a scene tree from the shots obtained earlier. The scene tree is shown to capture some

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semantic information as well as to provide a construct for hierarchical browsing of compressed videos.
Finally, a new model for video similarity based on global as well as local motion associated with each nodein the scene tree was constructed via new approaches on camera motion and object motion estimation.
Their experimental results demonstrated that the integration of the above techniques results in an efficient
framework for browsing and searching large video databases.
Mei-Ling Shyu, Shu-Ching Chen, Min Chen, and Chengcui Zhang addressed the need to search andretrieve images efficiently and accurately from a large image database or a collection ofimage databases in their paper entitled “A Unified Framework for Image Database Clustering andContent-based Retrieval.” A unified framework called Markov Model Mediators (MMMs) was proposed
to facilitate database clustering and improve the query processing performance by analyzing thesummarized knowledge. The unique characteristic of MMMs is that it provides the capabilities of exploring
the affinity relations among the images and databases at the intra-database level and inter-database level
using an effective data mining process. In this framework, at the intra-database level, each database ismodeled by a local MMM which enables accurate image retrieval within the database; while at the interdatabase level, conceptual database clustering is performed based on these local MMMs to group thoserelated databases into a conceptual image database cluster, represented by an integrated MMM. The
framework was tested using a set of image databases, which contain various numbers of images withdifferent dimensions and concept categories, and the experimental results demonstrated that accurate
retrieval results with low query processing cost can be achieved.
Marat Fayzullin, V.S. Subrahmanian, Massimiliano Albanese, and Antonio Picariello discussed the conceptof a priority curve associated with a video in “The Priority Curve Algorithm for Video Summarization.”
They also developed an algorithm that can use the priority curve to create a summary (of a desired length)of any video. The created summary exhibits nice continuity properties and also avoids repetitions. The
priority curve algorithm (PCA) was implemented and compared with other summarization algorithms in theliterature. It is shown that PCA is faster than the existing algorithms and also produces better qualitysummaries. The quality of summaries was evaluated by a group of 200 students in Naples, Italy, whowatched soccer videos.
In “Semantic Retrieval of Multimedia Data,” Samira Hammiche, Salima Benbernou, Mohand-Said Hacid,and Athena Vakali proposed an approach that deals with the problem of finding multimedia data that
fulfills the requirements of user queries. It assumes both the user query and the multimedia data areexpressed by the MPEG-7 standard. However, the MPEG-7 formalism lacks the semantics and reasoning
support in many ways, such as the search of the implicit data cannot be achieved since its description isbased on XML schema. Therefore, they proposed a framework for querying multimedia data based on atree embedding algorithm, combining the MPEG-7 standard and an external ontology.
“Complementary Information Retrieval for Cross-Media News Content” by Qiang Ma, Akiyo Nadamoto,and Katsumi Tanaka discussed a new way of integrating cross-media news content such as televisionprograms and web pages. Cross-media news content was searched to find complementary items which
can provide additional information to the users who are interested in a particular topic. The complementary
news items not only are similar to the item the user is interested in, but also provide information in moredetail or from a different perspective. A novel content-representation model called the “topic structure”

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model was developed. Intuitively, a topic structure is made up of a pair of subject and content terms.
Subject terms denote the dominant terms of a news item. A content term is a term having strong cooccurrence relationships with the subject terms. Based on the topic structure, the information related to agiven news item (e.g., one in which the user is interested) from content, context,
and media complementation perspectives can be searched. They also described an application
system which concurrently presents a television news program along with complementary news articles tohelp the users understand news topics in greater detail and from multiple perspectives.
MPEG-7 documents, primarily for multimedia information exchange, are data-centric XML documents.
Due to its advantages, the relational DBMS is the best choice for storing such XML documents. Storing
XML data in relational DBMS can be classified into two classes of storage model: structure-mapping andmodel-mapping. However, the structure-mapping model cannot support complex Xpath-based query
efficiently and model mapping approach lacks the flexible capability in representing all kinds of datatypes.
To address this issue, Yang Chu, Liang-Tien Chia and Sourav Saha Bhowmick presented a new storage
approach, called SM3, in “Looking at Mapping, Indexing & Querying of MPEG-7 Descriptors in RDBMSwith SM3.” As an XML document, MPEG-7 document can be viewed as XML tree. Such a tree graph,
where the internal nodes are element types with element contents, represents the structure of documents
and can be viewed as nodes which are meaningful only for document traversal. In their approach, the leaf
node, which is a single -valued attribute or element type with text content, has little usage for XML tree
routing as it is the end-point of Xpath. So it can be viewed as the special node which only holds the value.
In this paper, SM3 was designed to use a model-mapping approach to store all internal nodes and astructure-mapping model to store all leaf nodes. SM3 integrates the advantages of those two models andhopefully avoids the main drawbacks from each method. In performance evaluation, SM3 with a puremodel-mapping method (XParent) and SM3 with a pure structure-mapping method (XML-DBMS) werecompared, and the initial experimental results are encouraging.
Multivariate time series (MTS) datasets are common in various multimedia, medical and financial
applications. Kiyoung Yang and Cyrus Shahabi proposed a similarity measure for MTS datasets in their“A PCA-based Similarity Measure for Multivariate Time Series” paper. Eros (Extended Frobenius norm)
applies Principal Component Analysis (PCA) to MTS datasets represented as matrices to generate
principal components and associated eigenvalues. These principal components and eigenvalues are thenused to compare the similarity between MTS matrices. Experiments that compared Eros with Euclidean
Distance (ED), Dynamic Time Warping (DTW), Weighted Sum SVD (WSSVD) and PCA similarity
factor (SPCA) in precision/recall on three datasets (2 real-world and 1 synthetic) were conducted to show
its superiority for MTS datasets.
In “Indexing of Variable Length Multi-attribute Motion Data,” Chuanjun Li, Gaurav Pradhan, Si-Qing
Zheng, and Balakrishnan Prabhakaran exploited the singular value decomposition (SVD) properties ofmulti-attribute matrix data to obtain a representative vector for a multi-attribute matrix to address themulti-attribute data indexing issue. Based on this representative vector, they proposed a simple andefficient interval-tree based index structure for indexing motion data with dozens of attributes. At eachtree level, only one component of the query vector needs to be checked during searching, comparing to all
the components of the query vector that get involved for an R-tree or its variants. Searching time isindependent of the number of pattern motions indexed by the tree, making the index structure well scalable

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to large databases. Their experiments show that up to 91~93% irrelevant motions can be pruned for aquery, and the query searching time is less than 30 microseconds with the existence of motion variations.
Video classification is an important step towards multimedia understanding. Most state-of-art approaches
which apply HMM to capture the temporal information of videos have the limitation by assuming that thecurrent state of a video depends only on the immediate previous state. Nevertheless, this assumption may
not hold for videos of various categories. In “VRules: An Effective Association-based Classifier forVideos,” Ling Chen, Sourav Saha Bhowmick, and Liang-Tien Chia presented an effective video classifier
which employs the association rule mining technique to discover the actual dependence relationshipbetween video states with promising classification performance. The discriminatory state transition
patterns mined from different video categories are then used to perform classification. Besides capturing
the association between states in the time space, their approach also captures the association between
low-level features in spatial dimension to further distinguish the semantics of videos.
In their paper entitled “Automatic Classification of Speech and Music Using Neural Networks,”Muhammad K. S. Khan, Wasfi G. Al-Khatib, and Muhammad Moinuddin addressed the need to classify
audio into categories such as speech or music, which is an important aspect of many multimedia document
retrieval systems. The motivation of their work is the evolving importance of automatic discrimination
between speech signal and music signal. In this paper, they proposed the use of the mean and variance ofthe discrete wavelet transform in addition to other features that have been used previously for audio
classification. A Multi-Layer Perceptron (MLP) Neural Networks was used as a classifier. Their initial
testing has shown encouraging results that indicate the viability of their approach.
Finally, Latifur Khan on behalf of Lei Wang and Li Liu presented their proposed top-down, subspaceclustering algorithm in “Automatic Image Annotation and Retrieval Using Subspace Clustering Algorithm.”
Their approach tries to address the problem that image data usually have a large number of dimensions but
the traditional clustering algorithms assign equal weights to these dimensions, and become confounded in
the process of dealing with these dimensions. In their approach, for a given cluster, the relevant features
based on histogram analysis were determined and greater weights were assigned to the relevant features
as compared to less relevant features. In their experiments, four different models to link the visual tokens
with the keywords based on the clustering results of their clustering algorithm and the K-means algorithm
were implemented and evaluated using the precision, recall and correspondence accuracy under abenchmark dataset. The results showed that their algorithm is better than the traditional ones for automatic
image annotation and retrieval.
In addition to these spectacular sessions, the workshop also held one keynote speech by Prof. ArifGhafoor at Purdue University. The title of this keynote speech was “Web-Based Multimedia Databases:
Prospects and Challenges.” This talk elaborated the key challenges of developing Web-based multimedia
database applications, including (1) content analysis and indexing of distributed multimedia data anddocuments, (2) semantic modeling and knowledge-based representation of multimedia data, (3)transformation and organization of multimedia data semantics as a part of Semantic Web, (4) security,
privacy and QoS related issues concerning Web-based multimedia database applications, and (5) emerging
Web standards and their role in managing distributed multimedia databases. Several solutions and tools thathave been developed for Web-based multimedia database systems were also discussed.

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The long-term goal of MMDB is to build up a reputable and respectable workshop for the international
community. Without the contribution and support from the international community, MMDB could never
be classified as a significant and successful workshop as what the MMDB 2004 has achieved!
Furthermore, given the high quality of the papers, several selected papers are invited for the special issue
on “Multimedia Databases” in the Information Systems Journal (Elsevier).
Many people contributed to the success of this event. We would like to thank all the participants, paper
presenters, and session chairs for their valuable cooperation with the demands placed on them. We wish to
thank the program committee members and the external referees for their great work. We are grateful toProfessor Ghafoor for accepting to give a keynote presentation. Finally, we would like to express ourgratitude towards the CIKM organizers.

Shu-Ching Chen and Mei-Ling Shyu
ACM MMDB 2004 Program co-chairs

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