Visual Feature Space Analysis for Unsupervised Effectiveness Estimation and Feature Engineering

Abstract

The Feature Vector approach is one of the most popular schemes for managing multimedia data. For many data types such as audio, images, or 3D models, an abundance of different Feature Vector extractors are available. The automatic (unsupervised) identification of the best suited feature extractor for a given multimedia database is a difficult and largely unsolved problem. We here address the problem of comparative unsupervised feature space analysis. We propose two interactive approaches for the visual analysis of certain feature space characteristics contributing to estimated discrimination power provided in the respective feature spaces. We apply the approaches on a database of 3D objects represented in different feature spaces, and we experimentally show the methods to be useful (a) for unsupervised comparative estimation of discrimination power and (b) for visually analyzing important properties of the components (dimensions) of the respective feature spaces. The results of the analysis are useful for feature selection and engineering.