Scale and the differential structure of images
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Recognizing corners by fitting parametric models
International Journal of Computer Vision
A computational approach for corner and vertex detection
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
The Topological Structure of Scale-Space Images
Journal of Mathematical Imaging and Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
The hierarchical structure of images
IEEE Transactions on Image Processing
Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Multi-class multi-scale stacked sequential learning
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.