TR-2013-02
Image Classification with Reconfigurable Spatial Structures
Sobhan Naderi Parizi. 19 April, 2013.
Communicated by Pedro Felzenszwalb.
Abstract
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.
Original Document
The original document is available in PDF (uploaded 19 April, 2013 by
Pedro Felzenszwalb).
