Title :
Using discriminant eigenfeatures for image retrieval
Author :
Swets, D.L. ; Weng, John
Author_Institution :
Dept. of Comput. Sci., Augustana Coll., Sioux Fall, SD, USA
fDate :
8/1/1996 12:00:00 AM
Abstract :
This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as “well-framed” views, and compare it with that of the principal component analysis
Keywords :
face recognition; image recognition; information retrieval; object recognition; visual databases; discriminant eigenfeatures; image retrieval; image training set; multidimensional discriminant analysis; optimal linear projection; principal component analysis; view-based class retrieval; well-framed views; Content based retrieval; Image analysis; Image databases; Image retrieval; Image storage; Information retrieval; Management information systems; Principal component analysis; Shape; Spatial databases;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
