Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image Segmentation by Branch-and-Mincut
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Image Segmentation with a Unified Graphical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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The aim of semantic segmentation is to assign each pixel a semantic label. Numerous methods for semantic segmentation have been proposed in recent years and most of them chose pixel or superpixel as the processing primitives. However, as the information contained in a pixel or a superpixel is not discriminative enough, the outputs of these algorithms are usually not object consistent. To tackle this problem, we introduce the concept of object-like regions as a new and higher level processing primitive. We first experimentally showed that using groundtruth segments as processing primitives can boost semantic segmentation accuracy, and then proposed a novel method to produce regions that resemble the groundtruth regions, which we named them as 'object-like regions'. We achieve this by integrating state of the art low-level segmentation algorithms with typical semantic segmentation algorithms through a novel semantic feature feedback mechanism. We present experimental results on the publicly available image understanding dataset MSRC21 and stanford background dataset, showing that the new method can achieve relatively good semantic segmentation results with far fewer processing primitives.