We use the color cooccurrence histogram (CH) for recognizing objects in images. The color CH keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image
space. The color CH adds geometric information to the normal color histogram, which abstracts away all geometry. We compute model CHs based on images of known objects taken from different points of view. These model CHs are then matched to subregions in test images to find the object. By adjusting the number of colors and the number of distances used in the CH, we
can adjust the tolerance of the algorithm to changes in
lighting, viewpoint, and the flexibility of the object. We
develop a mathematical model of the algorithm’s false
alarm probability and use this as a principled way of
picking most of the algorithm’s adjustable parameters.
We demonstrate our algorithm on different objects,
showing that it recognize objects in spite of confusing
background clutter, partial occlusions, and flexing of
the object.
