Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Image classification using hybrid neural networks
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Old Fashioned State-of-the-Art Image Classification
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Bringing User Satisfaction to Media Access: The IST BUSMAN Project
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Classifier learning with a new locality regularization method
Pattern Recognition
Classifier learning with a new locality regularization method
Pattern Recognition
The theoretical foundations of statistical learning theory based on fuzzy number samples
Information Sciences: an International Journal
Using a non-uniform self-selective coder for option pricing
Applied Soft Computing
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
An annotation rule extraction algorithm for image retrieval
Pattern Recognition Letters
A new matching strategy for content based image retrieval system
Applied Soft Computing
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Image classification arises as an important phase in the overall process of automatic image annotation and image retrieval. In this study, we are concerned with the design of image classifiers developed in the feature space formed by low level primitives defined in the setting of the MPEG-7 standard. Our objective is to investigate the discriminatory properties of such standard image descriptors and look at efficient architectures of the classifiers along with their design pursuits. The generalization capabilities of an image classifier are essential to its successful usage in image retrieval and annotation. Intuitively, it is expected that the classifier should achieve high classification accuracy on unseen images that are quite ''similar'' to those occurring in the training set. On the other hand, we may assume that the performance of the classifier could not be guaranteed in the case of images that are very much dissimilar from the elements of the training set. To follow this observation, we develop and use a concept of the localized generalization error and show how it guides the design of the classifier. As image classifier, we consider the usage of the radial basis function neural networks (RBFNNs). Through intensive experimentation we show that the resulting classifier outperforms other classifiers such as a multi-class support vector machines (SVMs) as well as ''standard'' RBFNNs (viz. those developed without the guidance offered by the optimization of the localized generalization error). The experimental studies reveal some interesting interpretation abilities of the RBFNN classifiers being related with their receptive fields.