Stationary Features and Cat Detection

作者：François Fleuret, Donald Geman, François Fleuret, Donald Geman · 发表于：Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 年份：2007 · 被引用次数：62 · 研究领域：Domain Adaptation and Few-Shot Learning、Advanced Image and Video Retrieval Techniques、Robotics and Sensor-Based Localization

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. The efficiency of this strategy for a complex pose, i.e., for fine-grained descriptions, can be assessed by measuring the effect of sample size and pose resolution on accuracy and computation. Two conclusions emerge: i) fragmenting the training data, which is inevitable in dealing with high in-class variation, severely reduces accuracy; ii) the computational cost at high resolution is prohibitive due to visiting a massive pose partition. To overcome data-fragmentation we propose a novel framework centered on pose-indexed features which assign a response to a pair consisting of an image and a pose, and are designed to be stationary: the probability distribution of the response is always the same if an object is actually present. Such features allow for efficient, one-shot learning of pose-specific classifiers. \\\\ To avoid expensive scene processing, we arrange these classifiers in a hierarchy based on nested partitions of the pose as in previous work, 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...