Constrained parametric min-cuts for automatic object segmentation

• Published in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 3241 - 3248
• Main Authors: Carreira, Joao, Sminchisescu, Cristian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Computer vision; Humans; Image segmentation; Machine learning; Machine learning algorithms; Mathematics; Numerical simulation; Object segmentation; Predictive models
• ISBN: 1424469848; 9781424469840
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2010.5540063

We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge about properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict how plausible the segments are, given their mid-level region properties. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC09 segmentation dataset. It achieves the same average best segmentation covering as the best performing technique to date, 0.61 when using just the top 7 ranked segments, instead of the full hierarchy in. Our method achieves 0.78 average best covering using 154 segments. In a companion paper, we also show that the algorithm achieves state-of-the art results when used in a segmentation-based recognition pipeline.