PCA-Based Relevance Feedback in Document Image Retrieval
This is an incremental improvement for document image retrieval systems, enhancing accuracy through interactive feedback.
The paper tackles improving document image retrieval systems by incorporating relevance feedback, specifically using PCA to update feature subspaces, resulting in better performance compared to common methods.
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.