On the Effects of Distance Functions to Improve Content-based Image Retrieval
Resumo
The retrieval of images by visual content relies on a feature extractor to provide the most meaningful intrinsic characteristics (features) from the data, and a distance function to quantify the similarity between them. A challenge in this field supporting content-based image retrieval (CBIR) to answer similarity queries is how to best integrate these two key aspects. There are plenty of researching on algorithms for feature extraction of images. However, little attention have been paid to the importance of the use of a well-suited distance function associated to a feature extractor. This Master Dissertation was conceived to fill in this gap. It was also proposed a new technique to perform feature selection over the feature vectors, in order to improve the precision when answering similarity queries. This work also showed that the proper use of a distance function effectively improves the similarity query results. Therefore, it opens new ways to enhance the acceptance of CBIR systems.
Referências
Balan, A. G. R., Traina, A. J. M., and Marques, P. M. d. A. (2005). Fractal analysis of image textures for indexing and retrieval by content. In 18th IEEE CBMS, pages 56–63, Dublin, Ireland.
Bugatti, P. H. and Traina, A. J. M. (2008). Analysis of the influence of distance functions to answer similarity queries in content-based image retrieval. Master’s dissertation, University of São Paulo, Computer Science Department, São Carlos, Brazil.
Bugatti, P. H., Traina, A. J. M., and Traina Jr., C. (2008a). Assessing the best integration between distance-function and image-feature to answer similarity queries. In Proceedings of the 23rd ACM SAC, pages 1225–1230, New York, NY, USA. ACM.
Bugatti, P. H., Ribeiro, M. X., Traina, A. J. M., and Traina Jr., C. (2008b). Content-based retrieval of medical images by continuous feature selection. In Proceedings of the 21st IEEE CBMS, pages 272–277, Washington, DC, USA. IEEE Computer Society.
Deserno, T. M., Antani, S., and Long, L. R. (2007). Gaps in content-based image retrieval. Medical Imaging 2007: PACS and Imaging Informatics, volume 6516 of SPIE.
Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE TSMC, 3:610–621.
Khotanzad, A. and Hong, Y. H. (1990). Invariant image recognition by zernike moments. IEEE TPAMI, 12(5):489–497.
Malcok, M., Aslandogan, Y., and Yesildirek, A. (2006). Fractal dimension and similarity search in high-dimensional spatial databases. In IEEE IRI 2006, pages 380–384, Waikoloa, Hawaii, USA.
Ribeiro, M. X., Balan, A. G. R., Felipe, J. C., Traina, A. J. M., and Traina Jr., C. (2005). Mining statistical association rules to select the most relevant medical image features. In IEEE MCD 2005, pages 91–98, Houston, USA.
Traina, A. J. M., Traina Jr., C., Bueno, J. M., and Marques, P. M. d. A. (2002a). The metric histogram: A new and efficient approach for content-based image retrieval. In Sixth IFIP VDB, volume 216 of IFIP Conference Proceedings, pages 297–311, Brisbane, Australia.
Traina Jr., C.,Traina, A. J. M., Faloutsos, C., and Seeger, B. (2002b). Fast indexing and visualization of metric datasets using slim-trees. IEEE TKDE, 14(2):244–260.
