  Most image-search approaches today are based on the text based tags
associated with the images which are mostly human generated and are subject to
various kinds of errors. The results of a query to the image database thus can
often be misleading and may not satisfy the requirements of the user. In this
work we propose our approach to automate this tagging process of images, where
image results generated can be fine filtered based on a probabilistic tagging
mechanism. We implement a tool which helps to automate the tagging process by
maintaining a training database, wherein the system is trained to identify
certain set of input images, the results generated from which are used to
create a probabilistic tagging mechanism. Given a certain set of segments in an
image it calculates the probability of presence of particular keywords. This
probability table is further used to generate the candidate tags for input
images.
