An Image-Based Approach to Visual Feature Space Analysis
Date issued
2008
Journal Title
Journal ISSN
Volume Title
Publisher
Václav Skala - UNION Agency
Abstract
Methods for management and analysis of non-standard data often rely on the so-called feature vector approach. The technique
describes complex data instances by vectors of characteristic numeric values which allow to index the data and to calculate
similarity scores between the data elements. Thereby, feature vectors often are a key ingredient to intelligent data analysis
algorithms including instances of clustering, classification, and similarity search algorithms. However, identification of appropriate
feature vectors for a given database of a given data type is a challenging task. Determining good feature vector extractors
usually involves benchmarks relying on supervised information, which makes it an expensive and data dependent process. In
this paper, we address the feature selection problem by a novel approach based on analysis of certain feature space images.
We develop two image-based analysis techniques for the automatic discrimination power analysis of feature spaces. We evaluate
the techniques on a comprehensive feature selection benchmark, demonstrating the effectiveness of our analysis and its
potential toward automatically addressing the feature selection problem.
Description
Subject(s)
vizuální analytika, příznakové vektory, automatický výběr znaků, samoorganizující se mapy
Citation
WSCG '2008: Communication Papers: The 16-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic, February 4 - 7, 2008, p. 223-230.