Feature localization and search by object model under illumination change

Color object recognition methods that are based on image retrieval algorithms can handle changes of illumination via image normalization, e.g. simple color-channel-normalization or by forming a doubly-stochastic image matrix. However these methods fail if the object sought is surrounded by clutter. Rather than directly trying to nd the target, a viable approach is to grow a small number of feature regions called locales. These are de ned as a nondisjoint coarse localization based on image tiles. In this paper, locales are grown based on chromaticity, which is more insensitive to illumination change than is color. Using a diagonal model of illumination change, a least-squares optimization on chromaticity recovers the best set of diagonal coe cients for candidate assignments from model to test locales stored in a database. If locale centroids are also stored then, adapting a displacement model to include model locale weights, transformed pose and scale can be recovered. Tests on databases of real images show promising results for object query.