Feature Weighting for Improving Document Image Retrieval System Performance

06/06/2012
by   Mohammadreza Keyvanpour, et al.
0

Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the feature using coefficient of multiple correlations. Coefficient of multiple correlations can be used to describe the synthesized effects and correlation of each feature. The aim of this paper is to show that feature weighting increases the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 93.23 become 98.66

READ FULL TEXT

page 2

page 3

research
06/22/2019

Image Retrieval and Pattern Spotting using Siamese Neural Network

This paper presents a novel approach for image retrieval and pattern spo...
research
03/23/2017

Content-based similar document image retrieval using fusion of CNN features

Rapid increase of digitized document give birth to high demand of docume...
research
03/12/2021

A Novel Probability Weighting Method To Fit Gaussian Functions

Gaussian functions are commonly used in different fields, many real sign...
research
02/07/2017

Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study

The effectiveness of three stop words lists for Arabic Information Retri...
research
03/20/2018

Adaptive Co-weighting Deep Convolutional Features For Object Retrieval

Aggregating deep convolutional features into a global image vector has a...
research
04/20/2018

Benchmarking Top-K Keyword and Top-K Document Processing with T^2K^2 and T^2K^2D^2

Top-k keyword and top-k document extraction are very popular text analys...
research
04/16/2021

Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction

Term weighting schemes are widely used in Natural Language Processing an...

Please sign up or login with your details

Forgot password? Click here to reset