  In this paper, we introduce a novel fusion method that can enhance object
detection performance by fusing decisions from two different types of computer
vision tasks: object detection and image classification. In the proposed work,
the class label of an image obtained from image classification is viewed as
prior knowledge about existence or non-existence of certain objects. The prior
knowledge is then fused with the decisions of object detection to improve
detection accuracy by mitigating false positives of an object detector that are
strongly contradicted with the prior knowledge. A recently introduced novel
fusion approach called dynamic belief fusion (DBF) is used to fuse the detector
output with the classification prior. Experimental results show that the
detection performance of all the detection algorithms used in the proposed work
is improved on benchmark datasets via the proposed fusion framework.
