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MBC-based shape retrieval: basics, optimizations, and open problems

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Abstract

Shape of an object is an important feature for image and multimedia similarity retrievals. In our previous studies we introduced a new \(boundary\)-\(based\) technique (\(MBC\)-\(based\)) for shape retrieval and compared its performance to other techniques. In this study, we describe in detail the basics of our \(MBC\)-\(based\) shape representation techniques, and we show how they support different query types. In addition, we describe two original optimization techniques that can further improve the performance of our \(MBC\)-\(based\) methods in several aspects, and show that they are also applicable to other applications (e.g., pattern recognition techniques). Finally, we define open problems in the area (e.g., partial similarity) and provide some hints on how to approach those problems.

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Notes

  1. To eliminate numerical errors, we use a circle with a wide boundary rather than its line contour.

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Acknowledgments

This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0082826 (ITR), IIS-0238560 (CAREER) and IIS-0307908, and unrestricted cash gifts from Okawa Foundation and Microsoft. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Computer Engineering Department, Kuwait University, Safat, Kuwait

    Maytham Safar

  2. Integrated Media Systems Center, Department of Computer Science, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Cyrus Shahabi

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  1. Maytham Safar
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Correspondence to Maytham Safar.

Additional information

Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF’s Integrated Media Systems Center (IMSC) at the University of Southern California. He received his B.S. degree in Computer Engineering from Sharif University of Technology in 1989 and his M.S. and Ph.D. degree in Computer Science from the University of Southern California in 1993 and 1996, respectively. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases, GIS and multimedia. Dr. Shahabi’s current research interests include Geospatial and Multidimensional Data Analysis, Peer-to-Peer Systems and Streaming Architectures. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems (TPDS) and on the editorial board of ACM Computers in Entertainment magazine. He is also in the steering committee of IEEE NetDB and ACM GIS. He serves on many conference program committees such as ACM SIGKDD 2006, IEEE ICDE 2006, ACM CIKM 2005, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers (PECASE). In 2001, he also received an award from the Okawa Foundations.

Maytham Safar is currently an Assistant Professor at the Computer Engineering Department at Kuwait University. He received his Ph.D. degree in Computer Science from the University of Southern California in 2000. He has one book and more than 25 articles, book chapters, and conference/journal papers in the areas of databases and multimedia. Dr. Safar’s current research interests include Peer-to-Peer Networks, Spatial Databases, Multidimensional Databases, and Geographic Information Systems. He served on many conferences as a reviewer and/or a scientific program committee member such as ICDCS, EURASIA-ICT, WWW/Internet, ICWI, ICME, AINA, WEBIST, IPSI, HPC&S, ICICT, i-Society, ET-WBC, ICDIM and iiWAS. He also served as a member on the editorial board or a reviewer for many journals such as IEEE TMJ, ACM Computing Reviews, JDIM, MTAP, IEEE TPAMI, and ACM MSJ.

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Safar, M., Shahabi, C. MBC-based shape retrieval: basics, optimizations, and open problems. Multimed Tools Appl 29, 189–206 (2006). https://doi.org/10.1007/s11042-006-0010-x

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