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 vari...

This paper addresses the problem of retrieving video sequences that contain a spatio-temporal pattern queried by a user. To achieve this, the visual content of each video sequence is first decomposed through the analysis of its local feature dynamics. Camera motion of the sequence, background and objects present in the captured scene and events occurring within it are represented respectively b...

The Relevance Feedback has been used to improve the performance of CBIR algorithms. This paper presents a relevance feedback method based on the regularized least squares classifier, and a technique to select feedback information in order to increase the learning rate. Experimental results are presented in the paper to illustrate the performance of the proposed relevance feedback method.

We tested two relevance feedback models, an adaptive linear model and a probabilistic model, using massive feedback query expansion in TREC-5 (Sumner & Shaw, 1997), experimented with a three-valued scale of relevance and reduced feedback query expansion in TREC-6 (Sumner, Yang, Akers & Shaw, 1998), and examined the effectiveness of relevance feedback using a subcollection and the effect of syst...

This document contains a description of experiments for the 2008 Relevance Feedback track. We experiment with different amounts of feedback, including negative relevance feedback. Feedback is implemented using massive weighted query expansion. Parsimonious query expansion using Dirichlet smoothing performs best on this relevance feedback track dataset. Additional blind feedback gives better res...

This paper considers the strategies of query expansion, relevance feedback and result fusion to increase both relevance and diversity in photo retrieval. In the text-based retrieval only experiments, the run with query expansion has better MAP and P20 than that without query expansion, and only has 0.85% decrease in CR20. Although relevance feedback run increases both MAP and P20, its CR20 decr...

This paper proposes a new approach for relevance feedback in content-based image retrieval systems. The proposed approaches combined the classical Rocchio relevance feedback with the Feature Relevance Estimation method. As such, according to the relevance feedback provided by the user, the algorithm performs a simultaneous query modification and a assignment of weights to all the components of ...

Relevance feedback is a powerful query modification technique in the field of content-based image retrieval. The key issue in relevance feedback is how to effectively utilize the feedback information to improve the retrieval performance. This paper presents a relevance feedback scheme using Bayesian network model for feedback information adoption. Relevant images during previous iterations are ...

Content-based image retrieval is a very dynamic study field, and in this field, how to improve retrieval speed and retrieval accuracy is a hot issue. The retrieval performance can be improved when applying relevance feedback to image retrieval and introducing the participation of people to the retrieval process. However, as for many existing image retrieval methods, there are disadvantages of r...