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Stationary Features and Folded Hierarchies for Efficient Object Detection

Stationary Features and Folded Hierarchies for Efficient Object Detection

This video was recorded at NIPS Workshop on Efficient Machine Learning, Whistler 2007. Most discriminative techniques for detecting instances from object categories in still images consist of looping over a partition of a pose space with dedicated binary classifiers. This strategy is inefficient for a complex pose, i.e., for fine-grained descriptions: i) fragmenting the training data, which is inevitable in dealing with high in-class variation, severely reduces accuracy; ii) the computational cost at high pose resolution is prohibitive due to visiting a massive pose partition. To overcome data-fragmentation I will discuss a novel framework centered on pose-indexed, stationary features, which allows for efficient, one-shot learning of pose-specific classifiers. Such features assign a response to a pair consisting of an image and a pose, and are designed so that the probability distribution of the response is constant if an object is actually present. To avoid expensive scene processing, the classifiers are arranged in a hierarchy based on nested partitions of the pose, which allows for efficient search. The hierarchy is then "folded" for training: all the classifiers at each level are derived from one base predictor learned from all the data. The hierarchy is "unfolded" for testing: parsing a scene amounts to examining increasingly finer object descriptions only when there is sufficient evidence for coarser ones. I will illustrate these ideas by detecting and localizing cats in highly cluttered greyscale scenes. This is joint work with Francois Fleuret.


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