A Fast and Simple Algorithm for Producing Candidate Regions

This paper addresses the task of producing candidate regions for detecting objects (e.g., car, cat) and background regions (e.g., sky, water). We describe a simple and rapid algorithm which generates a set of candidate regions

$\mathcal{C_R}$

by combining up to three ”selected-segments”. These are obtained by a hierarchical merging algorithm which seeks to identify segments corresponding to roughly homogeneous regions, followed by a selection stage which removes most of the segments, yielding a small subset of selected-segments

$\mathcal{S}$

. The hierarchical merging makes a novel use of the PageRank algorithm. The selection stage also uses a new criterion based on entropy gain with non-parametric estimation of the segments’ entropy. We evaluate on a new labeling of the Pascal VOC 2010 set where all pixels are labeled with one of 57 class labels. We show that most of the 57 objects and background regions can be largely covered by three of the selected-segments. We present a detailed per-object comparison on the task of proposing candidate regions with several state-of-the-art methods. Our performance is comparable to the best performing method in terms of coverage but is simpler and faster, and needs to output half the number of candidate regions, which is critical for a subsequent stage (e.g, classification).