  Research has been devoted in the past few years to relevance feedback as an
effective solution to improve performance of information retrieval systems.
Relevance feedback refers to an interactive process that helps to improve the
retrieval performance. In this paper we propose the use of relevance feedback
to improve document image retrieval System (DIRS) performance. This paper
compares a variety of strategies for positive and negative feedback. In
addition, feature subspace is extracted and updated during the feedback process
using a Principal Component Analysis (PCA) technique and based on user's
feedback. That is, in addition to reducing the dimensionality of feature
spaces, a proper subspace for each type of features is obtained in the feedback
process to further improve the retrieval accuracy. Experiments show that using
relevance Feedback in DIR achieves better performance than common DIR.
