Content-based retrieval using a multi-objective genetic algorithm

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Khoa Duc Tran: Content-based retrieval using a multi-objective genetic algorithm. IEEE SoutheastCon, 2005. 561 - 569.



Content-based retrieval from multimedia databases is an important multimedia research area where traditional keyword-based approaches are not adequate. Multimedia data is significantly different from alphanumeric data because multimedia data is generally meaningless to a human and multimedia objects are typically large. Moreover, the traditional keyword-based approaches require an enormous amount of human effort during manual annotation and maintaining the consistency of annotations throughout database evolution. Research on content-based retrieval focus on using low-level features like color and texture for image representation, and a geometric framework of distances in the feature space for similarity. However, systematic retrieval of the best matches in a large multimedia database requires exhaustive and exponential search and does not guarantee worst-case performance. In addition, it has been observed that certain image representation schemes perform better than others under certain query situations, and these schemes should be somehow integrated and adjusted on the fly to facilitate effective and efficient image retrieval. Some work has been done applying simple genetic algorithms for content-based retrieval to provide good, but not necessary optimal solutions. However, these simple genetic algorithms can find only one optimum solution in a single run. This research proposes a new content-based retrieval method based on a multi-objective genetic algorithm (MOGA), which is capable of finding multiple trade-off solutions in one run and providing a natural way for integrating multiple image representation schemes. This research focuses on structural similarity framework that addresses topological, directional and distance relations of image objects.

Extended Abstract


author={Khoa Duc Tran},
booktitle={Proceedings. IEEE SoutheastCon, 2005.},
title={Content-based retrieval using a multi-objective genetic algorithm},
keywords={content-based retrieval;genetic algorithms;image representation;image retrieval;multimedia databases;query formulation;visual databases;MOGA;alphanumeric data;annotation consistency;content-based retrieval;database evolution;directional relations;distance relations;exponential search;feature space;geometric distance framework;human effort;image color;image objects;image representation;image retrieval;image texture;keyword-based approaches;low-level features;manual annotation;multi-objective genetic algorithm;multimedia data;multimedia databases;multimedia objects;multiple image representation scheme integration;query situations;similarity;structural similarity framework;systematic retrieval;topological relations;trade-off solutions;worst-case performance;Content based retrieval;Genetic algorithms;Humans;Image representation;Image retrieval;Information retrieval;Multimedia databases;Multimedia systems;Performance evaluation;Prototypes},
url={ },

Used References

R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 2000, Addison-Wesley

D. Papadias, M. Mantzourogiannis, P. Kalnis, N. Mamoulis and I. Ahmad: Content-based retrieval using heuristic search. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999.

D. Papadias: Hill climbing algorithms for content-based retrieval of similar configurations. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000.

D. Arkoumanis, M. Terrovitis and L. Stamatogiannakis, "Heuristic Algorithms for Similar Configuration Retrieval in Spatial Databases", Processing of 2nd Hellenic Conference on Artificial Intelligence (SETN)

R. Zhao and W. I. Grosky, "Bridging the semanitic gap in image retrieval" in Distributed multimedia databases: techniques & applications, pp. 14-36, 2002, Idea Group Publishing

K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2002

M. J. Egenhofer and R. D. Franzosa, "Point-set topological spatial relations", International Journal on Geographical Information Systems, vol. 5, pp. 161-174, 1991

M. J. Egenhofer and J. Sharma, "Assessing the Consistency of Complete and Incomplete Topological Information", Geographical Systems, vol. 1, pp. 47-68, 1993

S. Skiadopoulos and M. Koubarakis, "Composing Cardinal Directions Relations", Proceedings of the 7th International Symposium on Spatial and Temporal Databases (SSTD-01)

D. Papadias, N. Arkoumanis and N. Karacapilidis, "On The Retrieval of Similar Configurations", Proceedings of 8th International Symposium on Spatial Data Handling (SDH)

J. F. Allen, "Maintaining knowledge about temporal intervals", Communications of the ACM, vol. 26, pp. 832-843, 1983

A. Yoshitaka and T. Ichikawa, "A Survey on Content-Based Retrieval for Multimedia Databases", IEEE Transaction on Knowledge and Data Engineering, vol. 11, pp. 81-93, 1999

V. Delis, D. Papadias and N. Mamoulis, "Assessing multimedia similarity: a framework for structure and motion", Proceedings of the sixth ACM international conference on Multimedia

C. Freksa, "Temporal Reasoning based on Semi Intervals", Artificial Intelligence, vol. 54, 1992

A. Mackworth and E. Freuder, "The complexity of some polynomial network consistency algorithms for constraint satisfaction problems", Artificial Intelligence, vol. 25, pp. 65-74, 1985

K. Deb, "Genetic Algorithm in Search and Optimization: The Technique and Applications", Indian Institute of Technology, Kanpur, 1999

K. Deb and S. Agrawal, "Understanding interactions among genetic algorithm parameters", Foundations of Genetic Algorithms 5, pp. 265-286, 1998

D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989, Addison-Wesley

T. Bäck, "Optimal mutation rates in genetic search", Proceeding of the Fifth International Conference on Genetic Algorithms

D. J. Schaffer, R. A. Caruana, L. J. Eshelman and R. Das, "A study of control parameters affecting online performance of Genetic Algorithms for function optimization", Proceedings of the third international conference on Genetic Algorithms

S. Kato and S.-i. Iisaku: An Image Retrieval Method Based on a Genetic Algorithm. Information Networking, 1998. (ICOIN-12) Proceedings., Twelfth International Conference on, (ICOIN'98), 1998, pp. 333–336

J.-Y. Lee and S.-B. Cho: Interactive Genetic Algorithm for Content-Based Image Retrieval. Proceeding of Asian Fuzzy Systems Symposium (AFSS'98)

D. E. Goldberg and K. Sastry, "A Practical Schema Theorem for Genetic Algorithm Design and Tuning", Proceedings of the Genetic and Evolutionary Computation Conference

N. Beckmann, H. Kriegel, R. Schneider and B. Seeger, "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles", Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data

K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, 2001, John Wiley & Sons

C. A. Coello, "An Updated Survey of GA-Based Multiobjective Optimization Techniques", ACM Computing Surveys, vol. 32, 2000

C. A. Coello, "A Short Tutorial on Evolutionary Multiobjective Optimization", First International Conference on Evolutionary Multi-Criterion Optimization

C. A. Coello and G. T. Pulido, "Multiobjective Optimization using a Micro-Genetic Algorithm", Proceedings of the Genetic and Evolutionary Computation Conference (GECCO?2001)

D. Thierens and P. A. N. Bosman, "Multi-Objective Mixture-based Iterated Density Estimation Evolutionary Algorithms", Proceedings of the Genetic and Evolutionary Computation Conference (GECCO?2001)

K. Deb, "Evolutionary algorithms for multi-criterion optimization in engineering design", Proceeding Evolutionary Algorithms in Engineering and Computer Science (EUROGEN'99)

N. Srinivas and K. Deb, "Multiple objective optimization using nondominated sorting in genetic algorithms", Evolutionary Computation, vol. 2, pp. 221-248, 1994

T. Ray, T. Kang and S. Chye, "Muttiobjective design optimization by an evolutionary algorithm", Engineering Optimization, 2002

M. Tanaka, H. Watanabe, Y. Furukawa and T. Tantrio, "GA-based decision support system for multi-criteria, optimization", Proceedings of the International Conference on Systems, Man and Cybernetics-2

K. D. Tran, "Elitist Non-Dominated Sorting GA-II (NSGA-II) as a Parameter-less Multi-Objective GA", presented at IEEE SoutheastCon 2005, Fort Lauderdale


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