Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Machine Learning
Wavelet-based image indexing techniques with partial sketch retrieval capability
IEEE ADL '97 Proceedings of the IEEE international forum on Research and technology advances in digital libraries
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
Information Sciences: an International Journal - Special issue on advanced neuro-fuzzy techniques and their applications
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Classifier fitness based on accuracy
Evolutionary Computation
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge discovery in multimedia repositories: the role of metadata
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
Creating a robust image classification system depends on having enough data with which one can adequately train and validate the model. If there is not enough available data, this assumption may not hold and would result in a classifier that exhibits poor performance, thus lowering it's acceptability. This paper offers a solution to the problem of training and testing a neuro-fuzzy system for the purpose of image recognition when there are a limited number of images. Features of interest are segmented from each image and then used to train a neural-fuzzy system. This increases the number of data examples used to train the system. The neuro-fuzzy system is then tested on the entire data set set of full images. A high level of classification accuracy has been obtained using this method. This solution has two advantages; one, it overcomes the problem of limited data examples for training a classification model and two, rules can be extracted from the neuro-fuzzy model for further analysis. We apply this system to the problem to detection of pest damage on images of apples in New Zealand orchards.