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Image browsing with PCA-assisted user-interaction

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  • Data Visualisation
  • Eigenvalues And Eigenfunctions
  • Image Retrieval
  • Principal Component Analysis
  • Real-Time Systems
  • Relevance Feedback
  • Search Engines
  • User Interfaces
  • Image Browsing
  • Pca-Assisted User-Interaction
  • Sophisticated Search Engines
  • Browsing Tool
  • User Search Intention
  • Real Time Systems
  • Visual Human System
  • Spatial Information
  • Virtual 3D Space
  • Image Features
  • Very High-Dimensional Mpeg-7 Descriptors
  • Visual Presentation
  • High-Dimensional Descriptor Components
  • Covariance-Matrix
  • Eigenvalues
  • Eigenspaces
  • Relevance Feedback Methods


User interfaces for sophisticated search engines must offer users quick and easy access to the objects to be visualized. We present a browsing tool which arranges images with respect to the user search intention in a continuous and intuitive manner in real time. Since the capacity of the visual human system is higher for spatial information, we prefer a virtual 3D space for the visualization. Because our image features are described in terms of very high-dimensional MPEG-7 descriptors, we have to reduce them to only three dimensions for visual presentation. The dimension reduction is realized by an appropriate weighting of the high-dimensional descriptor components corresponding to a modification of the covariance-matrix used for principal component analysis (PCA). In addition, this modification allows us to overcome a problem arising from equally sized eigenvalues and provides varying eigenspaces nearly continuously. The technique introduced is a general approach, which can be combined with other relevance feedback methods.

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