The indexing and retrieval of document images: a survey
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Information Retrieval from Documents: A Survey
Information Retrieval
Classification Method Study for Automatic Form Class Identification
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Page Segmentation for Manhattan and Non-Manhattan Layout Documents via Selective CRLA
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
A Rotation Invariant Page Layout Descriptor for Document Classification and Retrieval
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval.