Color-invariant shape moments for object recognition

Geometric moments have been widely used in many shape recognition and object classification tasks. These monomials are usually computed from binary or gray-level images for the object shape recognition invariant to rotation, translation, and scaling. In this paper, we attempt to calculate the shape related moments from color images, and study their noise immunity and color invariance property for the application areas of face recognition and content based image retrieval. To this end, we describe a computationally efficient method of converting a vector-valued color image into a gray scale for robust moment computation. Geometric moments are calculated from the resultant scalar representation of a color image data, and proven to be robust shape descriptors for the face and flower images. The generated shape invariants appear to have better noise immunity than the Hu moments and exhibit characteristics invariant to hue changes in the object colors. As compared to the Zernike polynomials, the proposed feature set has higher discriminatory power although the Zernike polynomials present superior noise rejection capability. Robust performance, computational efficiency, high noise immunity, and hue invariance property of the new approach are particularly useful for fast image retrieval tasks requiring high query accuracy.