@conference {13070,
	title = {A novel feature descriptor based on the shearlet transform},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {1033 - 1036},
	abstract = {Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.},
	keywords = {analysis;multiscale, analysis;object, classification;face, classification;image, classification;intensity, coefficients;image, descriptor;feature, detection;object, distribution, edge, EXTRACTION, extraction;image, gradient, gradients;histograms, identification;feature, methods;histograms, of, orientations;face, oriented, recognition;feature, recognition;shearlet, recognition;transforms;, shearlet, singularities;texture, texture;object, transform;signal},
	doi = {10.1109/ICIP.2011.6115600},
	author = {Schwartz, W.R. and da Silva,R.D. and Davis, Larry S. and Pedrini,H.}
}
@conference {14850,
	title = {Trainable 3D recognition using stereo matching},
	booktitle = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011///},
	pages = {625 - 631},
	abstract = {Stereo matching has been used for face recognition in the presence of pose variation. In this approach, stereo matching is used to compare two 2-D images based on correspondences that reflect the effects of viewpoint variation and allow for occlusion. We show how to use stereo matching to derive image descriptors that can be used to train a classifier. This improves face recognition performance, producing the best published results on the CMU PIE dataset. We also demonstrate that classification based on stereo matching can be used for general object classification in the presence of pose variation. In preliminary experiments we show promising results on the 3D object class dataset, a standard, challenging 3D classification data set.},
	keywords = {2D, 3D, class, classification, classification;image, data, dataset;CMU, dataset;face, descriptor;occlusion;pose, estimation;solid, image, image;3D, matching;pose, matching;trainable, modelling;stereo, object, PIE, processing;, recognition;face, recognition;image, set;3D, variation;stereo},
	doi = {10.1109/ICCVW.2011.6130301},
	author = {Castillo,C. D and Jacobs, David W.}
}
@conference {12472,
	title = {Evaluation of state-of-the-art algorithms for remote face recognition},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1581 - 1584},
	abstract = {In this paper, we describe a remote face database which has been acquired in an unconstrained outdoor environment. The face images in this database suffer from variations due to blur, poor illumination, pose, and occlusion. It is well known that many state-of-the-art still image-based face recognition algorithms work well, when constrained (frontal, well illuminated, high-resolution, sharp, and complete) face images are presented. In this paper, we evaluate the effectiveness of a subset of existing still image-based face recognition algorithms for the remote face data set. We demonstrate that in addition to applying a good classification algorithm, consistent detection of faces with fewer false alarms and finding features that are robust to variations mentioned above are very important for remote face recognition. Also setting up a comprehensive metric to evaluate the quality of face images is necessary in order to reject images that are of low quality.},
	keywords = {alarm;feature, algorithm;still, classification;image, classification;visual, database;remote, databases;, extraction;hidden, extraction;image, Face, feature, image-based, image;false, quality;occlusion;remote, recognition;face, recognition;feature, recognition;state-of-the-art, removal;image},
	doi = {10.1109/ICIP.2010.5652608},
	author = {Ni,Jie and Chellapa, Rama}
}
@conference {12519,
	title = {Learning multi-modal densities on Discriminative Temporal Interaction Manifold for group activity recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2450 - 2457},
	abstract = {While video-based activity analysis and recognition has received much attention, existing body of work mostly deals with single object/person case. Coordinated multi-object activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., are the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among multiple objects with a parametric Bayesian network, we propose a Discriminative Temporal Interaction Manifold (DTIM) framework as a data-driven strategy to characterize the group motion pattern without employing specific domain knowledge. In particular, we establish probability densities on the DTIM, whose element, the discriminative temporal interaction matrix, compactly describes the coordination and interaction among multiple objects in a group activity. For each class of group activity we learn a multi-modal density function on the DTIM. A Maximum a Posteriori (MAP) classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the approach.},
	keywords = {a, activities;parametric, activity, analysis;belief, analysis;pattern, Bayesian, classification;image, classifier;multimodal, complex, constraints;data-driven, density, equipment;video, function;multiobject, interaction, manifold;discriminative, matrix;football, MOTION, network;video-based, networks;image, play, posteriori, processing;, recognition, recognition;group, recognition;maximum, signal, spatial, strategy;discriminative, temporal},
	doi = {10.1109/CVPR.2009.5206676},
	author = {Ruonan Li and Chellapa, Rama and Zhou,S. K}
}
@conference {12551,
	title = {Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.},
	keywords = {algorithm;learning, analysis;computer, analysis;statistical, analysis;video, based, classification;image, classification;spatio-temporal, distribution, distributions;, Face, functions;shape, Grassmann, invariant, manifold;activity, manifold;Stiefel, matching;inference, matching;spatiotemporal, measures;estimation, modeling;statistical, parameters;pattern, phenomena;statistical, recognition;affine, recognition;computer, recognition;probability, SHAPE, structure;image, technique;geometric, theory;manifold-valued, vision;distance, vision;image},
	doi = {10.1109/CVPR.2008.4587733},
	author = {Turaga,P. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {13147,
	title = {Classifying Computer Generated Charts},
	booktitle = {Content-Based Multimedia Indexing, 2007. CBMI {\textquoteright}07. International Workshop on},
	year = {2007},
	month = {2007/06//},
	pages = {85 - 92},
	abstract = {We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.},
	keywords = {algorithm;scale, analysis;visual, classification;image, database;image, databases;, feature, Internet;bar-chart;curve-plot;image, invariant, match, matching;image, relationship;surface-plot;Internet;image, representation;image, segmentation;pie-chart;pyramid, segmentation;statistical, transform;scatter-plot;spatial},
	doi = {10.1109/CBMI.2007.385396},
	author = {Prasad,V. S.N and Siddiquie,B. and Golbeck,J. and Davis, Larry S.}
}
@conference {13148,
	title = {Objects in Action: An Approach for Combining Action Understanding and Object Perception},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those objects. While each of these can be conducted independently, recognition improves when interactions between these elements are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which unifies the inference processes involved in object classification and localization, action understanding and perception of object reaction. Traditional approaches for object classification and action understanding have relied on shape features and movement analysis respectively. By placing object classification and localization in a video interpretation framework, we can localize and classify objects which are either hard to localize due to clutter or hard to recognize due to lack of discriminative features. Similarly, by applying context on human movements from the objects on which these movements impinge and the effects of these movements, we can segment and recognize actions which are either too subtle to perceive or too hard to recognize using motion features alone.},
	keywords = {analysis;image, approach;action, Bayesian, classification;image, classification;object, framework;Bayes, interactions;inference, interpretation, localization;object, methods;gesture, MOTION, movements;human, perception;, perception;human-object, perception;object, process;object, processing;visual, recognition;image, recognition;video, segmentation;action, segmentation;object, signal, understanding;human},
	doi = {10.1109/CVPR.2007.383331},
	author = {Gupta,A. and Davis, Larry S.}
}
@article {14891,
	title = {Classification with nonmetric distances: image retrieval and class representation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {22},
	year = {2000},
	month = {2000/06//},
	pages = {583 - 600},
	abstract = {A key problem in appearance-based vision is understanding how to use a set of labeled images to classify new images. Systems that model human performance, or that use robust image matching methods, often use nonmetric similarity judgments; but when the triangle inequality is not obeyed, most pattern recognition techniques are not applicable. Exemplar-based (nearest-neighbor) methods can be applied to a wide class of nonmetric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques are ill-suited to deal with nonmetric dataspaces. We develop techniques for solving this problem, emphasizing two points: First, we show that the distance between images is not a good measure of how well one image can represent another in nonmetric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in nonmetric spaces, boundary points are less significant for capturing the structure of a class than in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance. We also suggest ways of applying parametric techniques to supervised learning problems that involve a specific nonmetric distance functions, showing how to generalize the idea of linear discriminant functions in a way that may be more useful in nonmetric spaces},
	keywords = {appearance-based, by, classification;image, correlation;correlation, dataspaces;nonmetric, distances;nonmetric, example;, functions;nonmetric, image, inequality;vector, judgments;robust, MATCHING, methods;image, methods;nonmetric, methods;triangle, points;boundary, points;class, representation;exemplar-based, representation;image, retrieval;learning, retrieval;nearest-neighbor, similarity, vision;atypical},
	isbn = {0162-8828},
	doi = {10.1109/34.862197},
	author = {Jacobs, David W. and Weinshall,D. and Gdalyahu,Y.}
}
