Simultaneous record detection and attribute labeling in web data extraction
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust scene recognition using language models for scene contexts
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Robust model-based scene interpretation by multilayered context information
Computer Vision and Image Understanding
Dynamic hierarchical Markov random fields and their application to web data extraction
Proceedings of the 24th international conference on Machine learning
Leveraging probabilistic season and location context models for scene understanding
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Putting Objects in Perspective
International Journal of Computer Vision
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
The Journal of Machine Learning Research
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Cue Integration for Urban Area Extraction in Remote Sensing Images
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Object boundary detection in images using a semantic ontology
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
Sequence labelling in structured domains with hierarchical recurrent neural networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Image modeling using tree structured conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using visual context and region semantics for high-level concept detection
IEEE Transactions on Multimedia - Special issue on integration of context and content
Scene categorization via contextual visual words
Pattern Recognition
Comparing compact codebooks for visual categorization
Computer Vision and Image Understanding
Context based object categorization: A critical survey
Computer Vision and Image Understanding
A spectral method for context based disambiguation of image annotations
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detecting object boundaries using low-, mid-, and high-level information
Computer Vision and Image Understanding
Learning conditional random fields for classification of hyperspectral images
IEEE Transactions on Image Processing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
A unified context assessing model for object categorization
Computer Vision and Image Understanding
Discriminative Models for Multi-Class Object Layout
International Journal of Computer Vision
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
Multiple region categorization for scenery images
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Semantic image segmentation with a multidimensional hidden markov model
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
International Journal of Computer Vision
A novel object categorization model with implicit local spatial relationship
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Road image segmentation and recognition using hierarchical bag-of-textons method
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
International Journal of Computer Vision
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Segmentation and classification of objects with implicit scene context
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
A generic model to compose vision modules for holistic scene understanding
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Hierarchical conditional random fields for myocardium infarction detection
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Hierarchical discriminative framework for detecting tubular structures in 3D images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Image annotation by modeling Supporting Region Graph
Applied Intelligence
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We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented.