The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying Join Candidates in the Cairo Genizah
International Journal of Computer Vision
Biologically inspired task oriented gist model for scene classification
Computer Vision and Image Understanding
Co-hierarchical analysis of shape structures
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Flat appearance-based systems, which combine clever image representations with standard classifiers, might be the most effective way to recognize objects using current technologies. In the future, however, it seems probable that hierarchical representations might have better performance. In such systems, the image representation consists of a sequence of sets of features, where each subsequent set is computed based on the previous sets. The main contributions of this paper are to: (1) pose the question "what is the best way to employ discriminative methods for hierarchical image representations?"; (2) enumerate some of the alternative hierarchies while drawing connections to recent work by brain researchers; (3) study experimentally the different alternatives. As we will show, the strategy used can make a substantial difference.