Abstract-In this paper, we propose a new scheme that merges color-and shape-invariant information for object recognition. To obtain robustness against photometric changes, color-invariant derivatives are computed first. Color invariance is an important aspect of any object recognition scheme, as color changes considerably with the variation in illumination, object pose, and camera viewpoint. These color invariant derivatives are then used to obtain similarity invariant shape descriptors. Shape invariance is equally important as, under a change in camera viewpoint and object pose, the shape of a rigid object undergoes a perspective projection on the image plane. Then, the color and shape invariants are combined in a multidimensional color-shape context which is subsequently used as an index. As the indexing scheme makes use of a color-shape invariant context, it provides a high-discriminative information cue robust against varying imaging conditions. The matching function of the color-shape context allows for fast recognition, even in the presence of object occlusion and cluttering. From the experimental results, it is shown that the method recognizes rigid objects with high accuracy in 3-D complex scenes and is robust against changing illumination, camera viewpoint, object pose, and noise. Index Terms-Color-shape context, composite information, geometric invariants, image retrieval, object recognition, photometric invariants.
Another one integrated approach called color-shape context by Diplaros et al. REF combines color and shape information for illumination and viewpoint invariant object recognition.
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Mathematics Medicine Computer Science