Definition
Both local (intra-image) and global (inter-class) similarities play complementary roles in image matching and ranking, so a simple linear combination scheme has been experimented with significant performance improvement over single image matching schemes.
Given an image retrieval system, the information need of a user can be modeled as the posterior probability of the set of relevant images R given an expression of the information need in the form of query specification q and an image x in the current database, P(R|q,x). The objective of the system is to return images with high probabilities of relevance to the user.
In Query By Example, P(R|q,x) depends on the similarity between query q and image x. On the other hand, we note that the set of relevant images R does not exist until a query has been specified. However we can construct prior categories of images Ck, k = 1, 2, …, M as some prototypical instances of R and compute the memberships of q and xto these prior...
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References
J.N. Kapur, and H.K. Kesava, “Entropy Optimization Principles with Applications,” Academic, 1992.
S.E. Robertson, and K.S. Jones, “Relevance Weighting of Search Terms,” Journal of the American Society for Information Sciences, Vol. 27, 1976, pp. 129–146.
J.H. Lim, and J.S. Jin, “Combining Intra-Image and Inter-Class Semantics for Consumer Image Retrieval,” Pattern Recognition, Vol. 38, No. 6, 2005, pp. 847–864.
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(2008). Combining Intra-Image and Inter-Class Semantics for Image Matching. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_265
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DOI: https://doi.org/10.1007/978-0-387-78414-4_265
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74724-8
Online ISBN: 978-0-387-78414-4
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