Unsupervised Categorization for Image Database Overview
Date:
Talk at Visual’02 conference, located in National Chiao-Tung University in Taiwan.
Abstract: We introduce a new robust approach to categorize image databases : Adaptative Robust Competition (ARC). Providing the best overview of an image database helps users browsing large image collections. Estimating the distribution of image categories and finding their most descriptive prototype represent the two main issues of image database categorization. Each image is represented by a high-dimensional signature in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Image database overview by categorization is computed in challenging conditions since clusters are overlapping and the number of clusters is unknown. Clustering is performed by minimizing a Competitive Agglomeration objective function with an extra noise cluster collecting outliers.