The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
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.
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Citations
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Metadata
- Authors
- Carreira, J., & Sminchisescu, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-07
- Volume
- 34
- Issue
- 7
- Pages
- 1312-1328
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2046382188
- Cited by
- 592
- References
- 7
- FWCI
- 33.12
- Type
- journal article
- Language
- en
- Open access
- closed
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