Locale-based visual object retrieval under illumination change

Providing a user with an effective image search engine has been a very active research area. A search by an object model is considered to be one of the most desirable and yet difficult tasks. An added difficulty is that objects can be photographed under different lighting conditions. We have developed a feature localization scheme that finds a set of locales in an image. We make use of a diagonal model for illumination change and obtain a candidate set of lighting transformation coefficients in chromaticity space. For each pair of coefficients, elastic correlation is performed, which is a form of correlation of locale colors. A least square minimization for pose estimation is then applied, followed by a process of texture support and shape verification. Tests on a database of over 1,400 images and video clips show promising image retrieval results. Moreover it has been shown that the method is capable of recovering lighting changes.