PhoneGuide: museum guidance supported by on-device object recognition on mobile phones
MUM '05 Proceedings of the 4th international conference on Mobile and ubiquitous multimedia
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Curious George: An attentive semantic robot
Robotics and Autonomous Systems
Foundations and Trends in Robotics
Object class detection using local image features and point pattern matching constellation search
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
A bayesian network approach to multi-feature based image retrieval
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
DietCam: Automatic dietary assessment with mobile camera phones
Pervasive and Mobile Computing
A shape based, viewpoint invariant local descriptor
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Unsupervised learning of visual feature hierarchies
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Design of a simultaneous mobile robot localization and spatial context recognition system
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Keypoints derivation for object class detection with SIFT algorithm
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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In this paper we present a method to recognize an object class by learning a statistical model of the class. The probabilistic model decomposes the appearance of an object class into a set of local parts and models the appearance, relative location, co-occurrence, and scale of these parts. However, in many object classification approaches that use local features, learning the parameters is exponential in the number of parts because of the problem of matching local features in the image to parts in the model. In this paper we present a learning method that overcomes this difficulty by adding new parts to the model incrementally, using the Maximum-Likelihood framework. When we add a part to the model, a set of candidate parts are selected and the part that increases the likelihood of the data the most is added to the model. Once this part is added to the model, the parameters for all parts up to this point are updated using EM. The learning and recognition in this approach are translation and scale invariant, robust to background clutter, and has less restriction on the number of parts in the model. The validity of the approach is demonstrated on a real world dataset, where the approach is competitive with others, and where the learning for a rich model is much faster than previous approaches.