On the robustness of color points of interest for image retrieval

For content-based image retrieval (CBIR), traditional approaches of image matching involve global descriptions of the color image. When considering particular tasks like object recognition or partial queries, more local characterizations must be employed. In this context, image description based on points of interest appear best adapted. The point characterization which proved reliable is based on combinations of the Hilbert´s differential invariants. For gray value images, such a description used to be considered up to third order. Generalizations to color images were previously proposed for stereovision and image retrieval. Some of them propose to consider the invariants only at first order, while others consider higher order invariants and compute some combinations of them to achieve illumination changes invariance. We discuss the advantages and drawbacks of these different choices, with the aim of proposing an optimal use of color points of interest for image retrieval.