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Showing papers by "Alexander C. Berg published in 2005"


Proceedings ArticleDOI
20 Jun 2005
TL;DR: This work approaches recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points, and shows results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.
Abstract: We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.

975 citations


Proceedings ArticleDOI
17 Oct 2005
TL;DR: A framework which can incorporate arbitrary pairwise constraints between body parts, such as scale compatibility, relative position, symmetry of clothing and smooth contour connections between parts is developed.
Abstract: The goal of this work is to recover human body configurations from static images. Without assuming a priori knowledge of scale, pose or appearance, this problem is extremely challenging and demands the use of all possible sources of information. We develop a framework which can incorporate arbitrary pairwise constraints between body parts, such as scale compatibility, relative position, symmetry of clothing and smooth contour connections between parts. We detect candidate body parts from bottom-up using parallelism, and use various pairwise configuration constraints to assemble them together into body configurations. To find the most probable configuration, we solve an integer quadratic programming problem with a standard technique using linear approximations. Approximate IQP allows us to incorporate much more information than the traditional dynamic programming and remains computationally efficient. 15 hand-labeled images are used to train the low-level part detector and learn the pairwise constraints. We show test results on a variety of images.

247 citations


Book ChapterDOI
TL;DR: Experimental results indicate that this generic model of local shape and deformation is applicable across a wide variety of object categories, providing good performance for object recognition and localization on a difficult object recognition benchmark.
Abstract: We address comparing related, but not identical shapes in images following a deformable template strategy. At the heart of this is the notion of an alignment between the shapes to be matched. The transformation necessary for alignment and the remaining differences after alignment are then used to make a comparison. A model determines what kind of deformations or alignments are acceptable, and what variation in appearance should remain after alignment. This ties strongly with the idea that the difference in shape is the residual difference, after some family of transformations has been applied for alignment. Finding an alignment of a model to a novel object involves search through the space of possible alignments. In many settings this search is quite difficult. This work shows that the search can be approximated by an easier discrete matching problem between key points on a model and a novel object. This is a departure from traditional approaches to deformable template matching that concentrate on analyzing differential models. This thesis presents theories and experiments on searching for, identifying, and using alignments found via discrete matchings. In particular we present a mathematical and ecological motivation for a medium scale descriptor of shape, geometric blur. Geometric blur is an average over transformations of a sparse signal or feature channel, and can be computed using a spatially varying convolution. The resulting shape descriptors are useful for evaluating local shape similarity. Experiments demonstrate their efficacy for image classification and shape correspondence. Finding alignments between shapes is formulated as an optimization problem over discrete matchings between feature points in images. Similarity between putative correspondences is measured using geometric blur, and the deformation in the configuration of points is measured by summing over deformations in pairwise relationships. The snatching problem is formulated as an integer quadratic programming problem and approximated with a simple technique. Experimental results indicate that this generic model of local shape and deformation is applicable across a wide variety of object categories, providing good (currently the best known) performance for object recognition and localization on a difficult object recognition benchmark. Furthermore this generic object alignment strategy can be used to model variation in images of an object category, identifying the repeated object structures and providing automatic localization of the objects.

155 citations