Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Image classification for content-based indexing
IEEE Transactions on Image Processing
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
Weighted centroid neural network for edge preserving image compression
IEEE Transactions on Neural Networks
Centroid Neural Network With a Divergence Measure for GPDF Data Clustering
IEEE Transactions on Neural Networks
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The automatic classification of images is an effective way to organize a large-scale image database storing thousands of image files. In this paper, an automatic content-based image classification model using Centroid Neural Networks (CNN) with a Divergence Measure called Divergence-based Centroid Neural Network (DCNN) is proposed. The DCNN algorithm, which employs the divergence measure as its distance measure, is used for clustering of Gaussian Probability Distribution Function (GPDF) data. In comparison with other conventional algorithms, the DCNN designed for probability data has the robustness advantages of utilizing a localized image representation method in which each image is represented by a Gaussian distribution feature vector. Experiments and results show that the proposed classification model yields accuracy improvements of 5.77% and 6.97% over models employing the conventional Divergence-based k-means (Dk-means) and Divergence-based Self Organizing Map (DSOM) algorithms, respectively.