Staff Members - Benha University
Weighted feature voting technique for content-based image retrieval
International Journal of Computational Vision and Robotics • 2018
Publication Information
Authors
Walaa E Elhady, Abdulwahab K Alsammak, Shady Y El-Mashad
Keywords
content based image retrieval; computational vision; feature extraction; hierarchical annular histogram; weighted average; matching measures; weighted feature voting
Journal
International Journal of Computational Vision and Robotics
Publisher
Inderscience Publishers (IEL)
Volume
8
Issue
3
Pages
283-299
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
A content-based image retrieval process is used to retrieve most
similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.
similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.