Feature extraction for CBIR and biometrics applications

Authors:
Ryszard S. Choras
Affiliations:
University of Technology & Life Sciences, Faculty of Telecommunications & Electrical Engineering, Bydgoszcz, Poland
Venue:
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Year:
2007

Citing 4
Cited 0

Local Grayvalue Invariants for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics: Personal Identification in Networked Society

Biometrics: Personal Identification in Networked Society
A human identification technique using images of the iris andwavelet transform

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In CBIR (Content-Based Image Retrieval), visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the query. In biometrics systems images used as patterns (e.g. fingerprint, iris, hand etc.) are also represented by feature vectors. The candidates patterns are then retrieved from database by comparing the distance of their feature vectors. The feature extraction methods for this applications are discussed.