Object detection using strongly-supervised deformable part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Mixture component identification and learning for visual recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Spring lattice counting grids: scene recognition using deformable positional constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Hierarchical space tiling for scene modeling
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
GIANT: geo-informative attributes for location recognition and exploration
Proceedings of the 21st ACM international conference on Multimedia
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We propose a new latent variable model for scene recognition. Our approach represents a scene as a collection of region models (“parts”) arranged in a reconfigurable pattern. We partition an image into a predefined set of regions and use a latent variable to specify which region model is assigned to each image region. In our current implementation we use a bag of words representation to capture the appearance of an image region. The resulting method generalizes a spatial bag of words approach that relies on a fixed model for the bag of words in each image region. Our models can be trained using both generative and discriminative methods. In the generative setting we use the Expectation-Maximization (EM) algorithm to estimate model parameters from a collection of images with category labels. In the discriminative setting we use a latent structural SVM (LSSVM). We note that LSSVMs can be very sensitive to initialization and demonstrate that generative training with EM provides a good initialization for discriminative training with LSSVM.