Semi-rotation invariant feature descriptors using Zernike moments for MLP classifier

In this paper, we focused on designing a semi-rotation invariant feature descriptors for classification problem. We proposed hierarchical Zernike moments architecture which is combination of original Zernike moments, the local receptive field concept and shared weights concept from convolution neural network. The descriptors which are output of the architecture have improved classification performance while maintaining rotation invariant property within semi-rotation region. Experimental results for classifying designed descriptors, extracted from semi-rotated MNIST dataset, with multi-layer perceptron demonstrate the effectiveness of the proposed method compared with other methods, such as original Zernike moments and convolution neural network.