TY - GEN
T1 - Learning the relative importance of features in image data
AU - Varde, Aparna
AU - Rundensteiner, Elke
AU - Javidi, Giti
AU - Sheybani, Ehsan
AU - Liang, Jianyu
PY - 2007
Y1 - 2007
N2 - In computational analysis in scientific domains, images are often compared based on their features, e.g., size, depth and other domain-specific aspects. Certain features may be more significant than others while comparing the images and drawing corresponding inferences for specific applications. Though domain experts may have subjective notions of similarity for comparison, they seldom have a distance function that ranks the image features based on their relative importance. We propose a method called FeaturesRank for learning such a distance function in order to capture the semantics of the images. We are given training samples with pairs of images and the extent of similarity identified for each pair. Using a guessed initial distance function, FeaturesRank clusters the given images in levels. It then adjusts the distance function based on the eiror between the clusters and training samples using heuristics proposed in this paper. The distance function that gives the lowest error is the output. This contains the features ranked in the order most appropriate the domain. FeaturesRank is evaluated with real image data from nanotechnology and bioinformatics. The results of our evaluation are presented in the paper.
AB - In computational analysis in scientific domains, images are often compared based on their features, e.g., size, depth and other domain-specific aspects. Certain features may be more significant than others while comparing the images and drawing corresponding inferences for specific applications. Though domain experts may have subjective notions of similarity for comparison, they seldom have a distance function that ranks the image features based on their relative importance. We propose a method called FeaturesRank for learning such a distance function in order to capture the semantics of the images. We are given training samples with pairs of images and the extent of similarity identified for each pair. Using a guessed initial distance function, FeaturesRank clusters the given images in levels. It then adjusts the distance function based on the eiror between the clusters and training samples using heuristics proposed in this paper. The distance function that gives the lowest error is the output. This contains the features ranked in the order most appropriate the domain. FeaturesRank is evaluated with real image data from nanotechnology and bioinformatics. The results of our evaluation are presented in the paper.
UR - http://www.scopus.com/inward/record.url?scp=48349093471&partnerID=8YFLogxK
U2 - 10.1109/ICDEW.2007.4400998
DO - 10.1109/ICDEW.2007.4400998
M3 - Conference contribution
AN - SCOPUS:48349093471
SN - 1424408326
SN - 9781424408320
T3 - Proceedings - International Conference on Data Engineering
SP - 237
EP - 244
BT - Workshops in Conjunction with the International Conference on Data Engineering - ICDE' 07
T2 - Workshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007
Y2 - 15 April 2007 through 20 April 2007
ER -