Image recognition based on invariant moment in the projection space

This paper proposes a projection-based invariant moment for image recognition. A set of features invariant to image translation and scaling are obtained in the 1-D projection space. For getting rotational invariance, rapid transform is employed. After obtaining the invariant feature vector, threshold analysis is used for feature data optimization, and principle component analysis (PCA) is applied for feature data length compression. Experimental results show the superiority of our method over Hu and other invariant moments.