Objectness estimation using edges

Generating object proposals before object detection has become a common way. In this paper, we present a novel method to measure the objectness of bounding boxes using edges. The contours play an important role in object localization and detection. The number of edges that are close to the boundary of a box has strong relationship with the likelihood of the box covering an object. In our method, we adopt a two-step scheme to generate object proposals. In the first step, we count the number of contours close to the box, where we use the proposed “Tile Algorithm” to wipe off the inner edges of a box. In the second step we re-rank the object proposals with a linear SVM classifier across all aspect-ratios for calibration. Experiments on the VOC2007 dataset show that we achieve 96.47% object detection rate with 1000 proposals.