Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Estimating the Support of a High-Dimensional Distribution
Neural Computation
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Ensemble one-class support vector machines for content-based image retrieval
Expert Systems with Applications: An International Journal
Training One-class Support Vector Machines in the Primal Space
ICECT '09 Proceedings of the 2009 International Conference on Electronic Computer Technology
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network
Neural Processing Letters
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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.