Multi-view multi-class object detection via exemplar compounding

To address the multi-view multi-class object detection problem, we propose a method named Vector Array Recognition by Indexing and Sequencing (VARIS). VARIS is able to find optimal similarity matching between the input image and pre-stored exemplars while allowing wide geometrical variations which are limited only by topology constraints. Aggregated similarity is further enhanced by matching the input image with compound exemplars. The exemplar compounding procedure also reduces the number of exemplars necessary for each class. Our experiments show that VARIS with exemplar compounding achieves state-of-the-art performance on PASCAL VOC2007 dataset with a reasonable computational cost.