  Classifiers embedded within human in the loop visual object recognition
frameworks commonly utilise two sources of information: one derived directly
from the imagery data of an object, and the other obtained interactively from
user interactions. These computer vision frameworks exploit human high-level
cognitive power to tackle particularly difficult visual object recognition
tasks. In this paper, we present innovative techniques to combine the two
sources of information intelligently for the purpose of improving recognition
accuracy. We firstly employ standard algorithms to build two classifiers for
the two sources independently, and subsequently fuse the outputs from these
classifiers to make a conclusive decision. The two fusion techniques proposed
are: i) a modified naive Bayes algorithm that adaptively selects an individual
classifier's output or combines both to produce a definite answer, and ii) a
neural network based algorithm which feeds the outputs of the two classifiers
to a 4-layer feedforward network to generate a final output. We present
extensive experimental results on 4 challenging visual recognition tasks to
illustrate that the new intelligent techniques consistently outperform
traditional approaches to fusing the two sources of information.
