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 pre-defined 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.
Parizi et al. REF used a reconfigurable model operated on a grid to capture the spatial information among regions.
Reconfigurable models for scene recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science