Clustering and aggregation of relational data with applications to image database categorization

  • Authors:

  • Hichem Frigui;Cheul Hwang;Frank Chung-Hoon Rhee

  • Affiliations:

  • Multimedia Research Laboratory, Department of Computer Engineering and Computer Science, University of Louisville, USA;Multimedia Research Laboratory, Department of Computer Engineering and Computer Science, University of Louisville, USA;School of Electrical Engineering and Computer Science, Hanyang University, South Korea

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

In this paper, we introduce a new algorithm for clustering and aggregating relational data (CARD). We assume that data is available in a relational form, where we only have information about the degrees to which pairs of objects in the data set are related. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings. CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. The cluster dependent relevance weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in subsequent steps of a learning system to improve its learning behavior. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. We represent the pairwise image dissimilarities by six different relational matrices that encode color, texture, and structure information.