Title :
Image retrieval based on similarity learning
Author :
El-Naqa, Issum ; Wernick, Miles N. ; Yang, Yongyi ; Galatsanos, Nikolas P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Abstract :
We explore the use of various learning algorithms to predict the user´s measure of similarity between a given query image and images in a database. Our aim is to obtain a similarity coefficient, for use in image retrieval, that more accurately reflects that of the user. The performance of a variety of learning machines was evaluated using statistical resampling to estimate the prediction error and retrieval effectiveness. The proposed approach was demonstrated using synthetic shape and texture examples. The results of the study are very promising, especially those obtained by the general regression neural network and the support vector machine/radial basis function method
Keywords :
feedforward neural nets; image matching; image retrieval; image texture; learning (artificial intelligence); learning automata; radial basis function networks; visual databases; feedforward neural network; general regression neural network; image database; image retrieval; image texture; learning algorithms; learning machines; performance evaluation; prediction error estimation; query image; radial basis function method; radial basis function network; similarity coefficient; similarity learning; statistical resampling; support vector machine; synthetic shape; user similarity measure prediction; Feature extraction; Feedforward neural networks; Image databases; Image retrieval; Information retrieval; Machine learning; Neural networks; Radial basis function networks; Spatial databases; Support vector machines;
Conference_Titel :
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-6297-7
DOI :
10.1109/ICIP.2000.899556
