Discrimination ability improvement of invariant moment based on multi-scale mathematical morphology

Abstract

Discrimination ability of invariant moment is crucial for similar object recognition and classification. To improve the discrimination ability of invariant moment for similar object classification, a multi-scale mathematical morphology based algorithm is proposed. Firstly, the multi-scale operations are used to extract the details of image at different scales. Then, the details of multi-scales are combined to form the very different part of the similar objects, and the corresponding invariant moments of the very different parts are calculated. Finally, a measure which is similar to the between-to within-class variance ratio is used to select the efficient invariant moments of the very different parts of several scales. The selected moments of the very different parts of the similar objects will be more efficient than the original invariant moments of original images. Hu moment is adopted as an example to show the efficiency of improvement. And, any type of invariant moment can be used to replace Hu moment in our algorithm to achieve a more efficient invariant moment algorithm. So, our proposed algorithm is very important and can be widely used in different similar object discrimination. The application of improved Hu moment by our algorithm are compared with the original Hu moment and wavelet moment, experimental results show that our algorithm is very efficient.