Multimodal fusion using learned text concepts for image categorization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
On the computational rationale for generative models
Computer Vision and Image Understanding
Foundations and Trends® in Computer Graphics and Vision
Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
People detection in image and video data
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Help-training semi-supervised LS-SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning Visual Object Categories for Robot Affordance Prediction
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Nonlinear Models Using Dirichlet Process Mixtures
The Journal of Machine Learning Research
Bottom-up and top-down object matching using asynchronous agents and a contrario principles
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Nonlinear regression model generation using hyperparameter optimization
Computers & Mathematics with Applications
A proposal for local and global human activities identification
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Weakly supervised classification of objects in images using soft random forests
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Object recognition using proportion-based prior information: Application to fisheries acoustics
Pattern Recognition Letters
Help-Training for semi-supervised support vector machines
Pattern Recognition
Sparse patch-histograms for object classification in cluttered images
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A generative model for multi class object recognition and detection
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Object categorization with sketch representation and generalized samples
Pattern Recognition
Sensor-driven agenda for intelligent home care of the elderly
Expert Systems with Applications: An International Journal
Generative object definition and semantic recognition
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Incremental face recognition: hybrid approach using short-term memory and long-term memory
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Level set evolution with locally linear classification for image segmentation
Pattern Recognition
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Object class detection: A survey
ACM Computing Surveys (CSUR)
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Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this paper we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use these models to illustrate the relative merits of the two approaches in the context of a data set of widely varying images of non-rigid objects (animals). Our results support the assertion that neither approach alone will be sufficient for large scale object recognition, and we discuss techniques for combining them.