A survey on vision-based human action recognition
Image and Vision Computing
Visual object-action recognition: Inferring object affordances from human demonstration
Computer Vision and Image Understanding
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Every picture tells a story: generating sentences from images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Robust sequence alignment for actor-object interaction recognition: Discovering actor-object states
Computer Vision and Image Understanding
Abstraction and generalization of 3D structure for recognition in large intra-class variation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
On importance of interactions and context in human action recognition: Nataliya,,Shapovalova
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Actions in stillweb images: visualization, detection and retrieval
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Learning from mistakes: object movement classification by the boosted features
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
FollowMe: enhancing mobile applications with open infrastructure sensing
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications
Synergistic methods for using language in robotics
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
Modeling complex temporal composition of actionlets for activity prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Learning human interaction by interactive phrases
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Learning to recognize daily actions using gaze
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Scene semantics from long-term observation of people
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Detecting actions, poses, and objects with relational phraselets
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Action recognition with exemplar based 2.5d graph matching
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Collective activity localization with contextual spatial pyramid
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
On recognizing actions in still images via multiple features
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Exploiting language models to recognize unseen actions
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Non-parametric hand pose estimation with object context
Image and Vision Computing
Large-scale web video shot ranking based on visual features and tag co-occurrence
Proceedings of the 21st ACM international conference on Multimedia
Discriminative hierarchical part-based models for human parsing and action recognition
The Journal of Machine Learning Research
Multi levels semantic architecture for multimodal interaction
Applied Intelligence
A non-command interface for automatic document provision during meetings
Proceedings of the companion publication of the 19th international conference on Intelligent User Interfaces
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Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene/event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape/appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.