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A unified framework for image database clustering and content-based retrieval

Published: 13 November 2004 Publication History

Abstract

With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called <i>Markov Model Mediators</i> (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.

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Fazli Can

A unified framework to support image database clustering and content-based image retrieval is proposed by the authors. For this purpose, they use the Markov model mediators (MMMs) mechanism to facilitate conceptual clustering. A MMM (triple M) is a stochastic finite state machine. Each state of a MMM is attached to a stochastic output process for describing the probability of occurrences of the output symbols (states). In the study, MMMs are used to explore affinity relations among the images at the database and cluster levels. The authors consider both effectiveness and efficiency issues, and provide, respectively, some reasonable results and a short discussion. For the latter, they ignore the off-line cost of clustering and related activities, with the assumption that they will be performed annually or semi-annually. This sounds quite unrealistic in dynamic environments, and needs further attention. The authors seek to address the problems that involve large databases, and, in their experiments, they use 12 databases, with a total of 18,700 images. This is a good experimental number, but it leaves a lot of ground to cover in terms of scalability. In their discussion, the authors use the term "distributed databases," and actually mean "autonomous databases," that is, databases that involve no interrelated integrity constraints. They state what they mean by "distributed databases" in their text. However, for clarity, they should have used either the phrase "autonomous databases" or "multi databases," instead of "distributed databases," throughout the paper. The material looks interesting, but it is hard to appreciate without additional reading. A nicely written, yet long general introduction could have been shortened, and the unused half-page at the end of this conference paper could have been used to provide more information about the authors' related earlier work. Online Computing Reviews Service

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cover image ACM Conferences
MMDB '04: Proceedings of the 2nd ACM international workshop on Multimedia databases
November 2004
118 pages
ISBN:1581139756
DOI:10.1145/1032604
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 November 2004

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Author Tags

  1. Markov model mediators (MMMs)
  2. content-based image retrieval (CBIR)
  3. image database clustering

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Cited By

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  • (2019)High Dimensional Latent Space Variational AutoEncoders for Fake News Detection2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2019.00088(437-442)Online publication date: Mar-2019
  • (2017)Classifier Fusion by Judgers on Spark Clusters for Multimedia Big Data ClassificationQuality Software Through Reuse and Integration10.1007/978-3-319-56157-8_5(91-108)Online publication date: 17-Aug-2017
  • (2016)Enhancing Rare Class Mining in Multimedia Big Data by Concept Correlation2016 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2016.0062(281-286)Online publication date: Dec-2016
  • (2016)A Classifier Ensemble Framework for Multimedia Big Data Classification2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2016.88(615-622)Online publication date: Jul-2016
  • (2016)Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2016.87(601-608)Online publication date: Jul-2016
  • (2016)Semantic Concept Detection Using Weighted Discretization Multiple Correspondence Analysis for Disaster Information Management2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2016.82(556-564)Online publication date: Jul-2016
  • (2016)Weighted subspace modeling for semantic concept retrieval using gaussian mixture modelsInformation Systems Frontiers10.1007/s10796-016-9660-z18:5(877-889)Online publication date: 1-Oct-2016
  • (2015)Gaussian Mixture Model-Based Subspace Modeling for Semantic Concept RetrievalProceedings of the 2015 IEEE International Conference on Information Reuse and Integration10.1109/IRI.2015.50(258-265)Online publication date: 13-Aug-2015
  • (2015)Utilizing Indirect Associations in Multimedia Semantic RetrievalProceedings of the 2015 IEEE International Conference on Multimedia Big Data10.1109/BigMM.2015.89(72-79)Online publication date: 20-Apr-2015
  • (2013)Rule-Based Semantic Concept Classification from Large-Scale Video CollectionsInternational Journal of Multimedia Data Engineering and Management10.4018/jmdem.20130101034:1(46-67)Online publication date: 1-Jan-2013
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