Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Random Field Model for Integration of Local Information and Global Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric Scene Parsing via Label Transfer
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Structured class-labels in random forests for semantic image labelling
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we introduce the concept of local label descriptor, which is a concatenation of label histograms for each cell in a patch. Local label descriptors alleviate the label patch misalignment issue in combining structured label predictions for semantic image labeling. Given an input image, we solve for a label map whose local label descriptors can be approximated as a sparse convex combination of exemplar label descriptors in the training data, where the sparsity is regularized by the similarity measure between the local feature descriptor of the input image and that of the exemplars in the training data set. Low-level image over-segmentation can be incorporated into our formulation to improve efficiency. Our formulation and algorithm compare favorably with the baseline method on the CamVid and Barcelona datasets.