Edge-based structural features for content-based image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
MPEG-7: Overview of MPEG-7 Description Tools, Part 2
IEEE MultiMedia
Content-based image classification using a neural network
Pattern Recognition Letters
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Fusing MPEG-7 visual descriptors for image classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Multi class adult image classification using neural networks
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Multi-module image classification system
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
A new matching strategy for content based image retrieval system
Applied Soft Computing
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
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Neural networks have been commonly used for image classification problems by fusing input features extracted from multiple MPEG-7 descriptors. It is because they can provide better performance than those extracted from single descriptor. However the input feature dimension can be various according to MPEG-7 descriptors. Usually input features with large dimension are dominant over those with small dimension for generating outputs of the neural networks, even though their contribution to output is almost same. In order to solve the problem, we propose a fusion neural network classifier which divides each descriptor by the number of its input features. And we consider the importance of the input features in each descriptor during training the classifier. In the experimental section, we showed the analysis of our method and compared the performance of sports image classification with conventional neural network classifier, using six classes of sports images collected on the Internet.