Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images

Many computer vision applications involve algorithms that can be decomposed in two main steps. In a first step, events or objects are detected and, in a second step, detections are assigned to classes. Examples of such “detection plus classification” problems can be found in human pose classification, object recognition or action classification among others. In this paper, we focus on a special case: depth ordering on single images. In this problem, the detection step consists of the image segmentation, and the classification step assigns a depth gradient to each contour or a depth order to each region. We discuss the limitations of the classical Precision-Recall evaluation framework for these kind of problems and define an extended framework called “Precision-Recall-Classfication” (PRC). Then, we apply this framework to depth ordering problems and design two specific PRC measures to evaluate both the local and the global depth consistencies. We use these measures to evaluate precisely state of the art depth ordering systems for monocular images. We also propose an extension to the method of [2] applying an optimal graph cut on a hierarchical segmentation structure. The resulting system is proven to provide better results than state of the art algorithms.