Similarity Based Visualization of Image Collections

In literature, few content based multimedia retrieval systems take the visualization as a tool for exploring the collections. However, when searching for images without examples to start with, one needs to explore the data set. Up to now, most available systems just show random collections of images in 2D grid form. More recently, advanced techniques have been developed for browsing based on similarity. However, none of them analyze the problems that occur when visualizing large visual collections. In this paper, we make these problems explicit. From there, we establish three general requirements: overview, visibility, and data structure preservation. Solutions for each requirement are proposed. Finally, a system is presented and experimental results are given to demonstrate our theory and approach.

[1]  Remco C. Veltkamp,et al.  A Survey of Content-Based Image Retrieval Systems , 2002 .

[2]  Robert van Liere,et al.  Visualization of multidimensional data using structure preserving projection methods , 2003, Data Visualization: The State of the Art.

[3]  Simone Santini,et al.  Emergent Semantics through Interaction in Image Databases , 2001, IEEE Trans. Knowl. Data Eng..

[4]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[5]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[6]  Thomas S. Huang,et al.  3D MARS: immersive virtual reality for content-based image retrieval , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[7]  Qi Tian,et al.  Visualization and User-Modeling for Browsing Personal Photo Libraries , 2004, International Journal of Computer Vision.

[8]  Chaomei Chen,et al.  Content-based image visualization , 2000, 2000 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics.

[9]  Ben A. M. Schouten,et al.  Show Me What You Mean! Pariss: A CBIR-Interface that Learns by Example , 2000, VISUAL.

[10]  Li Yang,et al.  Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[12]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  RICHARD C. DUBES,et al.  How many clusters are best? - An experiment , 1987, Pattern Recognit..

[14]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Alfred O. Hero,et al.  Geodesic entropic graphs for dimension and entropy estimation in manifold learning , 2004, IEEE Transactions on Signal Processing.

[16]  Robert Pless,et al.  Image spaces and video trajectories: using Isomap to explore video sequences , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Ulrik Brandes,et al.  Experiments on Graph Clustering Algorithms , 2003, ESA.

[18]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[19]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[20]  Luigi Cinque,et al.  A Multidimensional Image Browser , 1998, J. Vis. Lang. Comput..

[21]  C. VERTAN,et al.  BROWSING IMAGE DATABASES BY 2D IMAGE SIMILARITY SCATTER PLOTS: UPDATES TO THE IRIS SYSTEM , 2002 .

[22]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[23]  G. Karypis,et al.  Criterion Functions for Document Clustering ∗ Experiments and Analysis , 2001 .