Personalizing automated image annotation using cross-entropy

Annotating the increasing amounts of user-contributed images
in a personalized manner is in great demand. However,
this demand is largely ignored by the mainstream of automated
image annotation research. In this paper we aim
for personalizing automated image annotation by jointly exploiting
personalized tag statistics and content-based image
annotation. We propose a cross-entropy based learning algorithm
which personalizes a generic annotation model by
learning from a user’s multimedia tagging history. Using
cross-entropy-minimization basedMonte Carlo sampling, the
proposed algorithm optimizes the personalization process in
terms of a performance measurement which can be flexibly
chosen. Automatic image annotation experiments with
5,315 realistic users in the social web show that the proposed
method compares favorably to a generic image annotation
method and a method using personalized tag statistics only.
For 4,442 users the performance improves, where for 1,088
users the absolute performance gain is at least 0.05 in terms
of average precision. The results show the value of the proposed
method.