QCluster: relevance feedback using adaptive clustering for content-based image retrieval

ABSTRACT

The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance feedback are currently available to process relatively complex queries on large image databases. In the case of complex image queries, the feature space and the distance function of the user's perception are usually different from those of the system. This difference leads to the representation of a query with multiple clusters (i.e., regions) in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space.In this paper, we propose a new content-based image retrieval method using adaptive classification and cluster-merging to find multiple clusters of a complex image query. When the measures of a retrieval method are invariant under linear transformations, the method can achieve the same retrieval quality regardless of the shapes of clusters of a query. Our method achieves the same high retrieval quality regardless of the shapes of clusters of a query since it uses such measures. Extensive experiments show that the result of our method converges to the user's true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 34% in recall and about 33% in precision better than that of the query point movement approach, in MARS.

REFERENCES

• 1: T. Ashwin, R. Gupta, and S. Ghosal. Adaptable similarity search using non-relevant information. In Proceedings of the 28th VLDB Conference. Hong Kong, China, August 2002.
• 2: Ilaria Bartolini , Paolo Ciaccia , Florian Waas, FeedbackBypass: A New Approach to Interactive Similarity Query Processing, Proceedings of the 27th International Conference on Very Large Data Bases, p.201-210, September 11-14, 2001
• 3: M. Bauer, U. Gather, and M. Imhoff. The identification of multiple outliers in online monitoring data. Technical Report 29, SFB 475, University of Dortmund, 1999.
• 4: Bir Bhanu , Anlei Dong, Concepts Learning with Fuzzy Clustering and Relevance Feedback, Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, p.102-116, July 25-27, 2001
• 5: R. Brunelli and O. Mich. Image retrieval by examples. IEEE Transactions on Multimedia, 2(3):164--171, September 2000.
• 6: The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces, Proceedings of the 15th International Conference on Data Engineering, p.440, March 23-26, 1999
• 7: Efficient Query Refinement in Multimedia Databases, Proceedings of the 16th International Conference on Data Engineering, p.196, February 28-March 03, 2000
• 8: Moses Charikar , Chandra Chekuri , Tomás Feder , Rajeev Motwani, Incremental clustering and dynamic information retrieval, Proceedings of the twenty-ninth annual ACM symposium on Theory of computing, p.626-635, May 04-06, 1997, El Paso, Texas, United States [doi>10.1145/258533.258657]
• 9: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 10: Myron Flickner , Harpreet Sawhney , Wayne Niblack , Jonathan Ashley , Qian Huang , Byron Dom , Monika Gorkani , Jim Hafner , Denis Lee , Dragutin Petkovic , David Steele , Peter Yanker, Query by Image and Video Content: The QBIC System, Computer, v.28 n.9, p.23-32, September 1995 [doi>10.1109/2.410146]
• 11: Yoshiharu Ishikawa , Ravishankar Subramanya , Christos Faloutsos, MindReader: Querying Databases Through Multiple Examples, Proceedings of the 24rd International Conference on Very Large Data Bases, p.218-227, August 24-27, 1998
• 12: R. A. Johnson , D. W. Wichern, Applied multivariate statistical analysis, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
• 13: Kriengkrai Porkaew , Kaushik Chakrabarti, Query refinement for multimedia similarity retrieval in MARS, Proceedings of the seventh ACM international conference on Multimedia (Part 1), p.235-238, October 30-November 05, 1999, Orlando, Florida, United States [doi>10.1145/319463.319613]
• 14: J. J. Rocchio. Relevance Feedback in Information Retrieval, in G. Salton ed., The SMART Retrieval System - Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs, N.J., 1971.
• 15: Y. Rui, T. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in mars. In Proceedings of IEEE International Conference on Image Processing '97. Santa Barbara, CA, October 1997.
• 16: Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 8(5):644--655, 1998.
• 17: Gerard Salton , Edward A. Fox , Harry Wu, Extended Boolean information retrieval, Communications of the ACM, v.26 n.11, p.1022-1036, Nov. 1983 [doi>10.1145/182.358466]
• 18: John R. Smith , Shih-Fu Chang, VisualSEEk: a fully automated content-based image query system, Proceedings of the fourth ACM international conference on Multimedia, p.87-98, November 18-22, 1996, Boston, Massachusetts, United States [doi>10.1145/244130.244151]
• 19: T. Wang, Y. Rui, and S.-M. Hu. Optimal adaptive learning for image retrieval. In Proceedings of IEEE CVPR 2001, pages 1140--1147. Kauai, Hawaii, 2001.
• 20: Leejay Wu , Christos Faloutsos , Katia P. Sycara , Terry R. Payne, FALCON: Feedback Adaptive Loop for Content-Based Retrieval, Proceedings of the 26th International Conference on Very Large Data Bases, p.297-306, September 10-14, 2000
• 21: Xiang Sean Zhou , Thomas S. Huang, Comparing discriminating transformations and SVM for learning during multimedia retrieval, Proceedings of the ninth ACM international conference on Multimedia, September 30-October 05, 2001, Ottawa, Canada [doi>10.1145/500141.500163]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

General Terms:
Algorithms, Design, Experimentation, Performance, Theory

Keywords:
classification, cluster-merging, content-based image retrieval, image database, relevance feedback