A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Image and Feature Co-Clustering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Visual guided navigation for image retrieval
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
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In this paper the associations between the low-level features and the images are utilized to create a visualization that enables the user to determine the nature of the database, e.g., what are the predominant features for a group of images and vice-versa. This is based on modeling the database with a bipartite graph and partitioning the graph to find partitions of images and low-level features. Moreover, by using the concept of orthogonal arrays, a sample of images from each partition are presented to the user as a visual snapshot of the database.