  The existing methods for image search reranking suffer from the
unfaithfulness of the assumptions under which the text-based images search
result. The resulting images contain more irrelevant images. Hence the re
ranking concept arises to re rank the retrieved images based on the text around
the image and data of data of image and visual feature of image. A number of
methods are differentiated for this re-ranking. The high ranked images are used
as noisy data and a k means algorithm for classification is learned to rectify
the ranking further. We are study the affect ability of the cross validation
method to this training data. The pre eminent originality of the overall method
is in collecting text/metadata of image and visual features in order to achieve
an automatic ranking of the images. Supervision is initiated to learn the model
weights offline, previous to reranking process. While model learning needs
manual labeling of the results for a some limited queries, the resulting model
is query autonomous and therefore applicable to any other query .Examples are
given for a selection of other classes like vehicles, animals and other
classes.
