Hypothesis verification with histogram of compositions improves object detection of hierarchical models

This paper focuses on applying and evaluating the additional hypothesis verification step for the detections of learnthierarchy-of-parts (LHOP) method. The applied method reduces the problem of false positives that are a common problem of hierarchical methods specifically in highly textured or cluttered images. We use a Histogram of Compositions (HoC) with a Support Vector Machine in hypothesis verification step. Using HoC descriptor ensures that the additional computation cost is as minimal as possible since HoC descriptor shares the LHOP tree structure. We evaluate the method on the ETHZ Shape Classes dataset and show that our method outperforms the original baseline LHOP method by around 5 percent.