In this research, we propose an object localization method to boost the performance of current object detection techniques. This method utilizes the image edge information as a clue to determine the location of the objects. The Generic Edge Tokens (GETs) of the image are extracted based on the perceptual organization elements of human vision. These edge tokens are parsed according to the Best First Search algorithm to fine-tune the location of objects, where the objective function is the detection score returned by the Deep Convolutional Neural Network. We have evaluated our method on top of the RCNN object detection method. The results on Pascal VOC 2007 and 2012 show improved object localization performance. We also present several cases where the proposed method works significantly more precisely than RCNN.