Cross-Resolution Image Similarity Modeling
Mladen Jović, Yutaka Hatakeyama, and Kaoru Hirota
Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
A cross-resolution image similarity model employing probabilistic interpretations of the similarity values turned into fuzzy values combined with suitable merge functions is presented. Masking the negative particularities of individual region-based image similarity models, five region-based image similarity models are used at the same time when calculating the overall image similarity. By employing aggregation operators, capturing of a variety of conjunctive, disjunctive, and other non-linear combinations of similarity criteria is allowed. Empirical evaluation of the proposed model on four test databases, containing 4,444 images in 150 semantic categories was carried out. The results obtained from the evaluation revealed that cross-resolution image similarity modeling results in optimal retrieval performance. Compared to two well-known image retrieval systems, SIMPLicity and WBIIS, the proposed model brings an increase of 1.7% and 22% respectively in average retrieval precision. The experimental evaluation presented may thus be helpful and suggest possible further improvements can be achieved along the same line of research directions in various computer vision tasks.
-  K. Porkaew, S. Mehrotra, M. Ortega, and K. Chakrabarti, “Similarity search using multiple examples in MARS,” Proc. Int. Conf. Vis. Inf. Syst., pp. 68-75, 1999.
-  M. Ortega et al., “Supporting ranked boolean similarity queries in MARS,” IEEE Transactions on Knowledge Data Engineering 10(6), pp. 905-925, 1998.
-  V. Castelli and L. D. Bergman, “Digital imagery: fundamentals,” in V. Castelli, L. D. Bergman (Eds.), Image Databases: Search and Retrieval of Digital Imagery, Wiley, New York, USA, Ch. 7, pp. 161-166, 2002.
-  V. Castelli and L. D. Bergman, “Digital imagery: fundamentals,” in V. Castelli, L. D. Bergman (Eds.), Image Databases: Search and Retrieval of Digital Imagery, Wiley, New York, USA, Ch. 14, pp. 375-376, 2002.
-  J. C. Pichel, D. E. Singh, and F. F. Rivera, “Image segmentation based on merging of sub-optimal segmentations,” Pattern Recognition Letters, (27)10, pp. 1105-1116, 2006.
-  M. Stricker and M. Orengo, “Similarity of color images,” Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases III, San Jose, CA, USA, pp. 381-392, 1995.
-  A. Natsev, R. Rastogi, and K. Shim, “WALRUS: A similarity retrieval algorithm for image databases,” Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 395-406, 1999.
-  M. Serata, Y. Hatakeyama, and K. Hirota, “Designing Image Retrieval System with the Concept of Visual Keys,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.2, pp. 136-144, 2006.
-  J. Li, J. Z. Wang, and G. Wiederhold, “IRM: Integrated Region Matching for Image Retrieval,” Proceedings of the 8th ACM Multimedia Conference (MM’00), Los Angeles, CA, USA, pp. 147-156, 2000.
-  Q. Iqbal and J. K. Aggarwal, “Feature Integration, Multi-image Queries and Relevance Feedback in Image Retrieval,” Proc. of the 6th Intl. Conf. on Vis. Inform. Systems (VISUAL 2003), Miami, Florida, pp. 467-474, 2003.
-  L. Zhu and A. Zhang, “Supporting multi-example image queries in image databases,” IEEE International Conference on Multimedia and Expo (II), pp. 697-700, 2000.
-  D. Dubois and H. Prade, “A Review of Fuzzy Set Aggregation Connectives,” Information Sciences, Vol.36, pp. 85-121, 1985.
-  A. Smeulders, W. M. Worring, M. Santini, S. Gupta, and A. Jain, “Content-based image retrieval at the end of the early years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), pp. 1349-1380, 2000.
-  Corel Corporation, Corel Gallery 3.0., 2000.
-  P. Brodatz, “Textures: a photographic album for artists and designers,” N.Y.: Dover Publications, pp. 40-46, 1966.
-  Y. Rubner, C. Tomasi, and L. J. Guibas, “A metric for distributions with applications to image databases,” Proc. ICCV 98, pp. 59-66, 1998.
-  S. Brandt, J. Laaksonen, and E. Oja, “Statistical shape features in content-based image retrieval,” Proc. of 15th Int. Conf. on Pattern Recognition (ICPR-2000), Vol.2, Barcelona, Spain, pp. 1066-1069, 2000.
-  J. Laaksonen, E. Oja, M. Koskela, and S. Brandt, “Analyzing lowlevel visual features using content-based image retrieval,” Proc. 7th Int. Conf. on Neural Information Processing (ICONIP’00), Taejon, Korea, pp. 1333-1338, 2000.
-  J. S. De Bonet, “Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images,” ACM SIGGRAPH Computer Graphics, (31), pp. 361-368, 1997.
-  C. E. Jacobs, A. Finkelstein, and D. H. Salesin, “Fast Multiresolution Image Querying,” Proc. of the 22nd annual conference on Computer graphics and interactive techniques, ACM Press New York, NY, USA, pp. 277-286, 1995.
-  J. Z. Wang, J. Lia, and G. Wiederhold, “SIMPLIcity: Semanticssensitive Integrated Matching for Picture LIbraries,” IEEE Trans. Pattern Analysis and Machine Intelligence, 23(9), pp. 947-963, 2001.
-  Massachusetts Institute of Technology, Media Lab, Vision Texture Database, 2001.
-  M. R. Rezaee, P. M. J. van der Zwet, B. P. F. Lelieveldt, R. J. van der Geest, and J. H. C. Reiber, “A Multiresolution Image Segmentation Technique Based on Pyramidal Segmentation and Fuzzy Clustering,” IEEE Transactions on Image Processing, 9(7), pp. 1238-1248, 2000.
-  Z. Stejić, Y. Takama, and K. Hirota, “Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback,” Signal Processing, 85(2), pp. 297-324, 2005.