Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding. However, state-of-the-art CRF-based models for image segmentation rely mostly on 2D object models to construct top-down high-order potentials. In this paper, we propose new top-down potentials for image segmentation and pose estimation based on the shape and volume of a 3D object model. We show that these complex top-down potentials can be easily decomposed into standard forms for efficient inference in both the segmentation and pose estimation tasks. Experiments on a car dataset show that knowledge of segmentation helps perform pose estimation better and vice versa.
The CRF model for image segmentation proposed in @cite_25 @cite_17 is defined on a pixel graph and uses costs based on shape, texture, color, location and edge cues. @cite_26 @cite_19 extended the CRF approach to include long-range interactions between non-adjacent nodes in the graph by using costs defined over larger cliques and in a hierarchical manner. @cite_7 extended the CRF approach to a superpixel graph, where an energy consisting of unary and pairwise potentials captures appearance over superpixel neighbourhoods and local interactions between adjacent superpixels. @cite_0 @cite_23 also introduced new top-down costs based on the bag-of-features classifier @cite_14 , which captures interactions between all regions of an image that have the same label.
@cite_26 @cite_14 @cite_7 @cite_0 @cite_19 @cite_23 @cite_25 @cite_17
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In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling problem, i.e., each pixel is assigned to one of a set of labels. Features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. The features are used for the purpose of initial classification with a multilayer perceptron. Then a CRF model is introduced for modeling the local and contextual information jointly in order to improve the segmentation. For the purpose of decreasing the time complexity, we perform labeling at superpixel level. In the CRF model, graph nodes are represented by superpixels. The label of each pixel is determined by the label of the superpixel to which it belongs. Experiments on three public datasets demonstrate that, compared to previous methods, the proposed method achieves more accurate segmentation results and is much faster. Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image. The resulting graphs have billions of edges, making traditional inference algorithms impractical. Our main contribution is a highly efficient approximate inference algorithm for fully connected CRF models in which the pairwise edge potentials are defined by a linear combination of Gaussian kernels. Our experiments demonstrate that dense connectivity at the pixel level substantially improves segmentation and labeling accuracy. Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image. The resulting graphs have billions of edges, making traditional inference algorithms impractical. Our main contribution is a highly efficient approximate inference algorithm for fully connected CRF models in which the pairwise edge potentials are defined by a linear combination of Gaussian kernels. Our experiments demonstrate that dense connectivity at the pixel level substantially improves segmentation and labeling accuracy. As evidenced by video segmentation and cosegmentation approaches, exploiting multiple images is key to the success of visual scene understanding. With the availability of increasingly large sets of images, there is a clear need for methods that can efficiently analyze the similarities and structure across huge numbers of image pixels. Furthermore, to make effective use of this data, these similarities should not just be considered locally between neighboring pixels, but between all pairs of pixels across all images. In this paper, we tackle this challenging scenario by introducing a semantic co-labeling approach that performs efficient inference in a fully-connected CRF defined over the pixels, or superpixels, of an image set. Our experimental evaluation demonstrates that our approach yields improved accuracy while coming at no additional computation cost compared to performing segmentation sequentially on individual images. Furthermore, our formulation lets us perform inference over ten thousand images in a matter of seconds.