title: CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts.
abstract: We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selection cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge of the properties of individual object classes, by solving a sequence of Constrained Parametric Min-Cut problems (CPMC) on a regular image grid. In a subsequent step, we learn to rank the corresponding segments by training a continuous model to predict how likely they are to exhibit real-world regularities (expressed as putative overlap with ground truth) based on their mid-level region properties, then diversify the estimated overlap score using maximum marginal relevance measures. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC 2009 and 2010 data sets. In our companion papers [1], [2], we show that the algorithm can be used, successfully, in a segmentation-based visual object category recognition pipeline. This architecture ranked first in the VOC2009 and VOC2010 image segmentation and labeling challenges.
authors.affiliation: Computer Vision and Machine Learning Group, Institute for Numerical Simulation, University of Bonn, Quinta da Fonte Nova, lote 39, 1 degree, Alcobac¸a 2460, Portugal. carreira@ins.uni-bonn.de
authors.forename: João
authors.initials: J
authors.lastname: Carreira
authors.forename: Cristian
authors.initials: C
authors.lastname: Sminchisescu