Learning the relative importance of features in image data

Aparna Varde, Elke Rundensteiner, Giti Javidi, Ehsan Sheybani, Jianyu Liang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


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.

Original languageEnglish
Title of host publicationWorkshops in Conjunction with the International Conference on Data Engineering - ICDE' 07
Number of pages8
StatePublished - 1 Dec 2007
EventWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey
Duration: 15 Apr 200720 Apr 2007


OtherWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007


Dive into the research topics of 'Learning the relative importance of features in image data'. Together they form a unique fingerprint.

Cite this