Image browsing with PCA-assisted user-interaction
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.