Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-based image retrieval using binary signatures
Proceedings of the 2002 ACM symposium on Applied computing
Computer
Parallel Pattern Recognition Computations within a Wireless Sensor Network
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Commodity-grid based distributed pattern recognition framework
AusGrid '08 Proceedings of the sixth Australasian workshop on Grid computing and e-research - Volume 82
Incremental Bayesian classification for multivariate normal distribution data
Pattern Recognition Letters
One shot associative memory method for distorted pattern recognition
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Space-dependent color gamut mapping: a variational approach
IEEE Transactions on Image Processing
Underwater target classification in changing environments using an adaptive feature mapping
IEEE Transactions on Neural Networks
A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs
IEEE Transactions on Neural Networks
A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition
IEEE Transactions on Neural Networks
An associative memory approach to medical decision support systems
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
Contemporary image recognition schemes either rely on single-feature recognition or focus on solving multi-feature recognition using complex computational approaches. Furthermore these approaches tend to be of tightly-coupled nature, thus not readily deployable within computational networks. Distributed Hierarchical Graph Neuron (DHGN) is a distributed single-cycle learning pattern recognition algorithm that can scale from coarse-grained to fine-grained networks and it has comparable accuracy to contemporary image recognition schemes. In this paper, we present an implementation of DHGN that works for multi-feature recognition of images. Our scheme is able to disseminate recognition of each feature within an image to a separate computational subnetwork. Thereby allowing a number of features being analysed simultaneously using a uniform recognition process. We have conducted tests on a collection of greyscale facial images. The results show that our approach produces high recognition accuracy through a simple distributed process. Furthermore, our approach implements single-cycle learning known as collaborative-comparison learning where new patterns are continuously stored using collaborative approach without affecting previously stored patterns. Our proposed scheme demonstrates higher classification accuracy in comparison with Back-Propagation Neural Network for multi-class images.