Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval

The pulse-coupled neural network (PCNN) has been widely used in image processing. The outputs of PCNN represent unique features of original stimulus and are invariant to translation, rotation, scaling and distortion, which is particularly suitable for feature extraction. In this paper, PCNN and intersecting cortical model (ICM), which is a simplified version of PCNN model, are applied to extract geometrical changes of rotation and scale invariant texture features, then an one-class support vector machine based classification method is employed to train and predict the features. The experimental results show that the pulse features outperform of the classic Gabor features in aspects of both feature extraction time and retrieval accuracy, and the proposed one-class support vector machine based retrieval system is more accurate and robust to geometrical changes than the traditional Euclidean distance based system.