[pdf manuscript] 80-citation
manuscript
[talk]
slides about the categorization system
[link] explaining
parallel pop-out of visual search
A
structure is decomposed with two principally different methods, the generation
of a local/global space for each
contour, and the generation of the symmetric
axes for image regions. The decomposition can also explain most pop-out phenomena of human visual
search.
Local/Global Space:
For each contour, a window is iterated through the contour, which classifies
whether a selected segment is a ‘bow’ or an ‘inflexion’, creating thereby
signatures for a given window size. This is carried out for different window
sizes to generate the local/global space, which contains a wealth of structural
information. Here the one for a wiggly arc:
Symmetric Axes: I
use a wave-propagation process to generate the symmetric
axes:
This
shows the full decomposition output for one image at different scales:
The
decomposition returns many parameters. The challenge is now to create a useful
multi-dimensional space, with which one can perform perfect categorization for
arbitrary images. I have already worked toward that direction by classifying
the images of the COREL and Caltech 101 collection:
[link
to image classification] shows
results from image classification (categorization), searches and sorting.
[link to COREL categorization] basic-level
categories sorted according to percentage correct
[link to COREL category labels] how we categorized the COREL image classes