@conference {14192,
	title = {Active scene recognition with vision and language},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {810 - 817},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a novel approach to utilizing high level knowledge for the problem of scene recognition in an active vision framework, which we call active scene recognition. In traditional approaches, high level knowledge is used in the post-processing to combine the outputs of the object detectors to achieve better classification performance. In contrast, the proposed approach employs high level knowledge actively by implementing an interaction between a reasoning module and a sensory module (Figure 1). Following this paradigm, we implemented an active scene recognizer and evaluated it with a dataset of 20 scenes and 100+ objects. We also extended it to the analysis of dynamic scenes for activity recognition with attributes. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.},
	keywords = {accuracy, active scene recognition, classification performance, Cognition, Computer vision, Detectors, Equations, high level knowledge utilization, HUMANS, image classification, inference mechanisms, object detectors, Object recognition, reasoning module, sensing process, sensory module, support vector machines, Training},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126320},
	author = {Yu,Xiaodong and Ferm{\"u}ller, Cornelia and Ching Lik Teo and Yezhou Yang and Aloimonos, J.}
}
@conference {12437,
	title = {Domain adaptation for object recognition: An unsupervised approach},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {999 - 1006},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.},
	keywords = {Data models, data representations, discriminative classifier, Feature extraction, Grassmann manifold, image sampling, incremental learning, labeled source domain, Manifolds, measurement, object category, Object recognition, Principal component analysis, sampling points, semisupervised adaptation, target domain, underlying domain shift, unsupervised approach, unsupervised domain adaptation, Unsupervised learning, vectors},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126344},
	author = {Gopalan,R. and Ruonan Li and Chellapa, Rama}
}
@conference {15066,
	title = {Learning a discriminative dictionary for sparse coding via label consistent K-SVD},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {1697 - 1704},
	abstract = {A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistent constraint called {\textquoteleft}discriminative sparse-code error{\textquoteright} and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse coding techniques for face and object category recognition under the same learning conditions.},
	keywords = {classification error, Dictionaries, dictionary learning process, discriminative sparse code error, face recognition, image classification, Image coding, K-SVD, label consistent, learning (artificial intelligence), object category recognition, Object recognition, optimal linear classifier, reconstruction error, singular value decomposition, Training data},
	doi = {10.1109/CVPR.2011.5995354},
	author = {Zhuolin Jiang and Zhe Lin and Davis, Larry S.}
}
@conference {12436,
	title = {Sparse dictionary-based representation and recognition of action attributes},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {707 - 714},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and class distribution for each dictionary item. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence we can describe an action video by a set of compact and discriminative action attributes. More importantly, we can recognize modeled action categories in a sparse feature space, which can be generalized to unseen and unmodeled action categories. Experimental results demonstrate the effectiveness of our approach in action recognition applications.},
	keywords = {action attributes, appearance information, class distribution, Dictionaries, dictionary learning process, Encoding, Entropy, Gaussian process model, Gaussian processes, Histograms, HUMANS, Image coding, image representation, information maximization, learning (artificial intelligence), modeled action categories, Mutual information, Object recognition, probabilistic logic, sparse coding property, sparse dictionary-based recognition, sparse dictionary-based representation, sparse feature space, unmodeled action categories},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126307},
	author = {Qiang Qiu and Zhuolin Jiang and Chellapa, Rama}
}
@article {12439,
	title = {Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {33},
	year = {2011},
	month = {2011/11//},
	pages = {2273 - 2286},
	abstract = {In this paper, we examine image and video-based recognition applications where the underlying models have a special structure-the linear subspace structure. We discuss how commonly used parametric models for videos and image sets can be described using the unified framework of Grassmann and Stiefel manifolds. We first show that the parameters of linear dynamic models are finite-dimensional linear subspaces of appropriate dimensions. Unordered image sets as samples from a finite-dimensional linear subspace naturally fall under this framework. We show that an inference over subspaces can be naturally cast as an inference problem on the Grassmann manifold. To perform recognition using subspace-based models, we need tools from the Riemannian geometry of the Grassmann manifold. This involves a study of the geometric properties of the space, appropriate definitions of Riemannian metrics, and definition of geodesics. Further, we derive statistical modeling of inter and intraclass variations that respect the geometry of the space. We apply techniques such as intrinsic and extrinsic statistics to enable maximum-likelihood classification. We also provide algorithms for unsupervised clustering derived from the geometry of the manifold. Finally, we demonstrate the improved performance of these methods in a wide variety of vision applications such as activity recognition, video-based face recognition, object recognition from image sets, and activity-based video clustering.},
	keywords = {activity based video clustering, activity recognition, computational geometry, Computational modeling, Data models, face recognition, feature representation, finite dimensional linear subspaces, geometric properties, Geometry, Grassmann Manifolds, Grassmann., HUMANS, Image and video models, image recognition, linear dynamic models, linear subspace structure, Manifolds, maximum likelihood classification, maximum likelihood estimation, Object recognition, Riemannian geometry, Riemannian metrics, SHAPE, statistical computations, statistical models, Stiefel, Stiefel Manifolds, unsupervised clustering, video based face recognition, video based recognition, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.52},
	author = {Turaga,P. and Veeraraghavan,A. and Srivastava, A. and Chellapa, Rama}
}
@article {14829,
	title = {Using specularities in comparing 3D models and 2D images},
	journal = {Computer Vision and Image Understanding},
	volume = {111},
	year = {2008},
	month = {2008/09//},
	pages = {275 - 294},
	abstract = {We aim to create systems that identify and locate objects by comparing known, 3D shapes to intensity images that they have produced. To do this we focus on verification methods that determine whether a known model in a specific pose is consistent with an image. We build on prior work that has done this successfully for Lambertian objects, to handle a much broader class of shiny objects that produce specular highlights. Our core contribution is a novel method for determining whether a known 3D shape is consistent with the 2D shape of a possible highlight found in an image. We do this using only a qualitative description of highlight formation that is consistent with most models of specular reflection, so no specific knowledge of an object{\textquoteright}s specular reflectance properties is needed. This allows us to treat non-Lambertian image effects as a positive source of information about object identity, rather than treating them as a potential source of noise. We then show how to integrate information about highlights into a system that also checks the consistency of Lambertian reflectance effects. Also, we show how to model Lambertian reflectance using a reference image, rather than albedos, which can be difficult to measure in shiny objects. We test each aspect of our approach using several different data sets. We demonstrate the potential value of our method of handling specular highlights by building a system that can locate shiny, transparent objects, such as glassware, on table tops. We demonstrate our hybrid methods on pottery, and our use of reference images with face recognition experiments.},
	keywords = {illumination, Object recognition, Specularity},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2007.12.004},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314207001713},
	author = {Osadchy,Margarita and Jacobs, David W. and Ramamoorthi,Ravi and Tucker,David}
}
@conference {18336,
	title = {Doubleshot: an interactive user-aided segmentation tool},
	booktitle = {Proceedings of the 10th international conference on Intelligent user interfaces},
	series = {IUI {\textquoteright}05},
	year = {2005},
	month = {2005///},
	pages = {287 - 289},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper, we describe an intelligent user interface designed for camera phones to allow mobile users to specify the object of interest in the scene simply by taking two pictures: one with the object and one without the object. By comparing these two images, the system can reliably extract the visual appearance of the object, which can be useful to a wide-range of applications such as content-based image retrieval and object recognition.},
	keywords = {Computer vision, mobile application, Object recognition},
	isbn = {1-58113-894-6},
	doi = {10.1145/1040830.1040901},
	url = {http://doi.acm.org/10.1145/1040830.1040901},
	author = {Tom Yeh and Darrell,Trevor}
}
@conference {18329,
	title = {A picture is worth a thousand keywords: image-based object search on a mobile platform},
	booktitle = {CHI {\textquoteright}05 extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}05},
	year = {2005},
	month = {2005///},
	pages = {2025 - 2028},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Finding information based on an object{\textquoteright}s visual appearance is useful when specific keywords for the object are not known. We have developed a mobile image-based search system that takes images of objects as queries and finds relevant web pages by matching them to similar images on the web. Image-based search works well when matching full scenes, such as images of buildings or landmarks, and for matching objects when the boundary of the object in the image is available. We demonstrate the effectiveness of a simple interactive paradigm for obtaining a segmented object boundary, and show how a shape-based image matching algorithm can use the object outline to find similar images on the web.},
	keywords = {content-based image retrieval, interactive segmentation, mobile interface, Object recognition},
	isbn = {1-59593-002-7},
	doi = {10.1145/1056808.1057083},
	url = {http://doi.acm.org/10.1145/1056808.1057083},
	author = {Tom Yeh and Grauman,Kristen and Tollmar,Konrad and Darrell,Trevor}
}
@conference {11904,
	title = {Contour migration: solving object ambiguity with shape-space visual guidance},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {330- 335 vol.1 - 330- 335 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A fundamental problem in computer vision is the issue of shape ambiguity. Simply stated, a silhouette cannot uniquely identify an object or an object{\textquoteright}s classification since many unique objects can present identical occluding contours. This problem has no solution in the general case for a monocular vision system. This paper presents a method for disambiguating objects during silhouette matching using a visual servoing system. This method identifies the camera motion(s) that gives disambiguating views of the objects. These motions are identified through a new technique called contour migration. The occluding contour{\textquoteright}s shape is used to identify objects or object classes that are potential matches for that shape. A contour migration is then determined that disambiguates the possible matches by purposive viewpoint adjustment. The technique is demonstrated using an example set of objects.},
	keywords = {Artificial intelligence, camera motion, CAMERAS, Computer vision, contour migration, Databases, edge detection, Intelligent robots, Laboratories, Machine vision, object ambiguity, Object recognition, pattern matching, Robot vision systems, servomechanisms, SHAPE, shape matching, shape-space visual guidance, silhouette matching, visual servoing},
	isbn = {0-7803-7398-7},
	doi = {10.1109/IRDS.2002.1041410},
	author = {Abd-Almageed, Wael and Smith,C.E.}
}
@conference {11894,
	title = {Hidden Markov models for silhouette classification},
	booktitle = {Automation Congress, 2002 Proceedings of the 5th Biannual World},
	volume = {13},
	year = {2002},
	month = {2002///},
	pages = {395 - 402},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use hidden Markov models for object classification from silhouettes is presented.},
	keywords = {Computer vision, Feature extraction, Fourier transforms, hidden Markov models, HMM, image classification, Neural networks, object classification, Object recognition, parameter estimation, pattern recognition, Probability distribution, Shape measurement, silhouette classification, Wavelet transforms},
	isbn = {1-889335-18-5},
	doi = {10.1109/WAC.2002.1049575},
	author = {Abd-Almageed, Wael and Smith,C.}
}
@conference {14169,
	title = {Hierarchical curve representation},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {143 - 146},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a robust method for describing planar curves in multiple resolution using curvature information. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The authors deal with the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the pyramid. Furthermore the resulting algorithm is conceptually simple and easily parallelizable. They develop theoretical results, analyzing the curvature of continuous curves in scale-space, which show the behavior of curvature extrema under varying scale. These results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Finally, experimental results demonstrate the potential of the method},
	keywords = {Automation, continuous curves, curvature, data mining, digital images, Educational institutions, Feature extraction, hierarchical curve representation, IMAGE PROCESSING, image recognition, image resolution, Image segmentation, multiresolution structure, Object recognition, planar curves, pyramid, Robustness, Sampling methods, Smoothing methods},
	isbn = {0-8186-2920-7},
	doi = {10.1109/ICPR.1992.201947},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {18307,
	title = {3D object recognition via simulated particles diffusion},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989. Proceedings CVPR {\textquoteright}89},
	year = {1989},
	month = {1989/06//},
	pages = {442 - 449},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A novel approach for 3D object recognition is presented. This approach is model-based, and assumes either 3D or 21/2 D scene acquisition. Transformation detection is accomplished along with an object identification (six degrees of freedom, three rotational and three translational, are assumed). The diffusion-like simulation recently introduced as a means for characterization of shape is used in the extraction of point features. The point features represent regions on the object{\textquoteright}s surface that are extreme in curvature (i.e. concavities and convexities). Object matching is carried out by examining the correspondence between the object{\textquoteright}s set of point features and the model{\textquoteright}s set of point features, using an alignment strategy. Triangles are constructed between all possible triples of object{\textquoteright}s point features, and then are aligned to candidate corresponding triangles of the model{\textquoteright}s point features. 21/2 range images are transformed into a volumetric representation through a parallel projection onto the 3-D space. The resultant volume is suitable for processing by the diffusion-like simulation},
	keywords = {3D object recognition, alignment strategy, Computational modeling, Computer science, data mining, Gold, Layout, Noise shaping, Object detection, Object recognition, parallel projection, pattern recognition, point features, radio access networks, scene acquisition, shape characterisation, Shape measurement, simulated particles diffusion, transformation detection},
	isbn = {0-8186-1952-x},
	doi = {10.1109/CVPR.1989.37886},
	author = {Yacoob,Yaser and Gold,Y. I}
}
