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Object Detection with Appearance-based Mixture Models Anonymous CVPR submission

Object detection is typically formulated as a binary classification problem in which the positive examples are bounding boxes containing instances of the object and negative examples are the randomly sampled boxes not containing the object. Due to large intra-class variation in the object pose, appearance and camera viewpoint, object detectors often have a tough time learning a single discriminative classifier that can achieve good performance on a challenging testset. To relieve the classifier of this Sisyphean burden, mixture models have been advocated, where positive examples are segregated into sub-categories such that all instances within a sub-category share a common characteristic. However, most approaches resort to heuristic techniques or rely on additional label/3D information for clustering the data. In this paper, we present a simple mixture model for object detection based on unsupervised appearance-based clustering of object instances. The approach is general, requiring no additional labels or heuristics, and it yields improved performance in the detection task.