Contour and skeleton are two main stream representations for shape recognition in the literature. It has been shown that such two representations convey complementary information, however combining them in a nature way is nontrivial, as they are generally abstracted by different structures (closed string vs graph), respectively. This paper aims at addressing the shape recognition problem by combining contour and skeleton into a mid-level of shape representation. To form a mid-level representation for shape contours, a recent work named Bag of Contour Fragments (BCF) is adopted; While for skeleton, a new mid-level representation named Bag of Skeleton Paths (BSP) is proposed, which is formed by pooling the skeleton codes by encoding the skeleton paths connecting pairs of end points in the skeleton. Finally, a compact shape feature vector is formed by concatenating BCF with BSP and fed into a linear SVM classifier to recognize the shape. Although such a concatenation is simple, the SVM classifier can automatically learn the weights of contour and skeleton features to offer discriminative power. The encouraging experimental results demonstrate that the proposed new shape representation is effective for shape classification and achieves the state-of-the-art performances on several standard shape benchmarks.
Generative models are also used for shape recognition. Sun and Super @cite_15 propose a Bayesian model, which use the normalized contour fragments as the input features for shape classification. Wang @cite_45 model shapes of one class by a skelet al prototype tree learned by skeleton graph matching. Then a Bayesian inference is used to compute the similarity between a testing skeleton and each skelet al prototype tree. Bai @cite_14 propose to integrate contour and skeleton by a Gaussian mixture model, in which contour fragments and skeleton paths are used as the input features. Unlike their method, ours encodes the contour and skeleton features into one shape descriptor according to the association between contour and skeleton. Therefore, we avoid the intractable step to finetune the weight between contour and skeleton models.
@cite_14 @cite_15 @cite_45
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We present a novel method for accurate marker-less capture of articulated skeleton motion of several subjects in general scenes, indoors and outdoors, even from input filmed with as few as two cameras. Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy. The discriminative part-based pose detection method, implemented using Convolutional Networks (ConvNet), estimates unary potentials for each joint of a kinematic skeleton model. These unary potentials are used to probabilistically extract pose constraints for tracking by using weighted sampling from a pose posterior guided by the model. In the final energy, these constraints are combined with an appearance-based model-to-image similarity term. Poses can be computed very efficiently using iterative local optimization, as ConvNet detection is fast, and our formulation yields a combined pose estimation energy with analytic derivatives. In combination, this enables to track full articulated joint angles at state-of-the-art accuracy and temporal stability with a very low number of cameras. In this paper, we introduce a framework for carrying object detection in different people from different views using pose preserving dynamic shape models. We model dynamic shape deformations in different people using kinematics manifold embedding and decomposable generative models by kernel map and multilinear analysis. The generative model supports pose-preserving shape reconstruction in different people, views and body poses. Iterative estimation of shape style and view with pose preserving generative model allows estimation of outlier in addition to accurate body pose. The model is also used for hole filling in the background-subtracted silhouettes using mask generated from the best fitting shape model. Experimental results show accurate estimation of carrying objects with hole filling in discrete and continuous view variations We develop a comprehensive Bayesian generative model for understanding indoor scenes. While it is common in this domain to approximate objects with 3D bounding boxes, we propose using strong representations with finer granularity. For example, we model a chair as a set of four legs, a seat and a backrest. We find that modeling detailed geometry improves recognition and reconstruction, and enables more refined use of appearance for scene understanding. We demonstrate this with a new likelihood function that rewards 3D object hypotheses whose 2D projection is more uniform in color distribution. Such a measure would be confused by background pixels if we used a bounding box to represent a concave object like a chair. Complex objects are modeled using a set or re-usable 3D parts, and we show that this representation captures much of the variation among object instances with relatively few parameters. We also designed specific data-driven inference mechanisms for each part that are shared by all objects containing that part, which helps make inference transparent to the modeler. Further, we show how to exploit contextual relationships to detect more objects, by, for example, proposing chairs around and underneath tables. We present results showing the benefits of each of these innovations. The performance of our approach often exceeds that of state-of-the-art methods on the two tasks of room layout estimation and object recognition, as evaluated on two bench mark data sets used in this domain. In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach (). Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40 of the time, compared to 10 for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset.