A region—based image database system using colour and texture
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Recognition Methods in Image and Video Databases: Past, Present and Future
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
The Adaptive Subspace Map for Texture Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Distortion tolerant pattern recognition based on self-organizing feature extraction
IEEE Transactions on Neural Networks
Texture Description by Independent Components
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
On the basis updating rule of adaptive-subspace self-organizing map (ASSOM)
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In this paper, a mixture-of-subspaces model is proposed to describe images. Images or image patches, when translated, rotated or scaled, lie in low-dimensional subspaces of the high-dimensional space spanned by the grey values. These manifolds can locally be approximated by a linear subspace. The adaptive subspace map is a method to learn such a mixture-of-subspaces from the data. Due to its general nature, various clustering and subspace-finding algorithms can be used. If the adaptive subspace map is trained on data extracted from images, a description of the image content is obtained, which can then be used for various classification and clustering problems. Here, the method is applied to an image database retrieval problem and an object image classification problem, and is shown to give promising results.