Abstract
We perform a multiperspective comparison of the retrieval performance of six variants of the region-based image similarity models. Our objective is to examine the effect on the retrieval performance, of: (1) the region matching approach, (2) the region weighting strategy, and (3) the number of regions. Common for the six variants is that: (1) images are uniformly partitioned into regions (i.e., rectangular blocks of equal size), at five different resolutions; and (2) from each region, color, shape, and texture features are extracted, and used for computing the region similarity. The difference between the variants is either in the region matching approach, or in the region weighting strategy. Regarding the region matching, the correspondence between pairs of regions of the two images is established based on either: (1) their spatial closeness, (2) their visual similarity, or (3) a combination of these. Regarding the region weighting, weights, as a function of distance between corresponding regions, either: (1) decrease linearly, (2) decrease exponentially, or (3) are constant. The evaluation of the six variants is performed on 5 test databases, containing 64,339 images, in 749 semantic categories. In total, 313,020 queries are executed, based on which the average (weighted) precision, recall, rank, and retrieval time are computed. Both the number of queries and the variety of the evaluation criteria make the evaluation more comprehensive than in the case of any of the existing works, dealing with the region-based image similarity models. Results of the evaluation reveal that, contrary to the expectations, the simplest variant results in the best overall retrieval performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bartolini, I., Ciaccia, P., Patella, M.: A sound algorithm for region-based image retrieval using an index. In: Ibrahim, M., Küng, J., Revell, N. (eds.) DEXA 2000. LNCS, vol. 1873, pp. 930–934. Springer, Heidelberg (2000)
Brandt, S., Laaksonen, J., Oja, E.: Statistical shape features in content-based image retrieval. In: Proc. 15th Int. Conf. on Pattern Recognition (ICPR-2000), Barcelona, Spain, vol. 2, pp. 1066–1069 (2000)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)
Chen, Y., Wang, J.Z.: A region-based fuzzy feature matching approach to contentbased image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 24(9), 1252–1267 (2002)
Corel Corp.: Corel Gallery 3.0 (2000), http://www3.corel.com/
Deng, Y., Manjunath, B.S.: An efficient low-dimensional color indexing scheme for region-based image retrieval. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 1999), Phoenix, AZ, USA, pp. 3017–3020 (1999)
Laaksonen, J., Oja, E., Koskela, M., Brandt, S.: Analyzing low-level visual features using content-based image retrieval. In: Proc. 7th Int. Conf. on Neural Information Processing (ICONIP 2000), Taejon, Korea, pp. 1333–1338 (2000)
MIT, Media Lab.: Vision Texture Database (2001), ftp://whitechapel.media.mit.edu/pub/VisTex/
Natsev, A., Rastogi, R., Shim, K.: WALRUS: a similarity retrieval algorithm for image databases. In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1999),Philadelphia, PA, USA, pp. 395–406 (1999)
Randen, T.: Brodatz Textures (2002), http://www.ux.his.no/tranden/brodatz.html
Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence. 22(12) (2000) 1349–1380
Smith, J.R., Li, C.S.: Image classification and querying using composite region templates. Int. Journal of Computer Vision and Image Understanding 75(1–2), 165–174 (1999)
Stejić, Z., Takama, Y., Hirota, K.: Genetic algorithm-based relevance feedback for image retrieval using Local Similarity Patterns. Information Processing and Management 39(1), 1–23 (2003)
Stricker, M., Orengo, M.: Similarity of color images. In: Proc. IS&T and SPIE Storage and Retrieval of Image and Video Databases III,San Jose, CA, USA, pp.381–392 (1995)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries. IEEE Trans. Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stejić, Z., Takama, Y., Hirota, K. (2004). Comprehensive Comparison of Region-Based Image Similarity Models. In: Christiansen, H., Hacid, MS., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2004. Lecture Notes in Computer Science(), vol 3055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25957-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-25957-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22160-9
Online ISBN: 978-3-540-25957-2
eBook Packages: Springer Book Archive