Abstract
Despite the efforts to reduce the semantic gap between user perception of similarity and feature-based representation of images, user interaction is essential to improve retrieval performances in content based image retrieval. To this end a number of relevance feedback mechanisms are currently adopted to refine image queries. They are aimed either to locally modify the feature space or to shift the query point towards more promising regions of the feature space. In this paper we discuss the extent to which query shifting may provide better performances than feature weighting. A novel query shifting mechanism is then proposed to improve retrieval performances beyond those provided by other relevance feedback mechanisms. In addition, we will show that retrieval performances may be less sensitive to the choice of a particular similarity metric when relevance feedback is performed.
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Del Bimbo A.: Visual Information Retrieval. Morgan Kaufmann Pub (1999)
Duda R.O., Hart P.E. and Stork D.G.: Pattern Classification. J. Wiley & Sons (2000)
Faloutsos C. and Lin K.: FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. Proc. of the 1995 ACM SIGMOD intern.l conf. on Management of data, San Jose, CA USA (1995) 163–174
Hesterkamp D.R., Peng J. and Dai H.K.: Feature relevance learning with query shining for content-based image retrieval. Proc. of the 15th IEEE Intern.l Conf. on Pattern Recognition (ICPR 2000) (2000) 250–253
Ishikawa Y., Subramanys R. and Faloutsos C: MindReader: Querying databases through multiple examples. Proc. of the 24th VLDB Conf, New York (1998) 433–438
Peng J., Bhanu B. and Qing S.: Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image Understanding. 75 (1999) 150–164
Rui Y., Huang T.S., and Mehrotra S.: Content-based image retrieval with relevance feedback: in MARS. Proc. IEEE intern.l conf. on Image Processing, Santa Barbara, CA (1997) 815–818
Rui Y., Huang T.S., Ortega M. and Mehrotra S.: Relevance Feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on Circuits and Systems for Video Technology 8 (1998) 644–655
Saltón G. and McGill M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1988)
Smeulders A.W.M., Worring M., Santini S., Gupta A. and Jain R.: Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22 (2000) 1349–1380
McG. Squire D., Müller W., Müller H. and Pun T.: Content-based query of image databases: inspirations from text retrieval. Pattern Recognition Letters 21 (2000) 1193–1198
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© 2001 Springer-Verlag Berlin Heidelberg
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Giacinto, G., Roli, F., Fumera, G. (2001). Adaptive Query Shifting for Content-Based Image Retrieval. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_27
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DOI: https://doi.org/10.1007/3-540-44596-X_27
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