A rotation and scale robust texture recognition using moment features and neural network

What is it about?

Texture is one of the key characteristics used to identify objects or regions of interest in an image. One of the difficulties in texture analysis has been the lack of appropriate tools for characterization. Previous texture analysis methods have been based on modified wavelet transforms, called tree-structured wavelet transforms, and are classified by neural networks. However, it is difficult to classify textures of different scales and rotations using only these methods. We propose texture recognition based on moment features. We compute the gray-level density function of each image and compare its histogram, which represents its shading. Histogram comparison is a commonly used method for indexing images and expressing the histogram in terms of its moments reduces the complexity of the method. For the computation of moment features, the wavelet transform corresponding to each node is used. For classification, each moment feature is fed to a neural network. This method allows the recognition of textures of different scales and rotations and improves the recognition rate.

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