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Geo-location Inference from Image Content and User Tags

Geo-location Inference from Image Content and User Tags

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Andrew Gallagher Dhiraj Joshi Jie Yu Jiebo Luo Kodak Research Laboratories Rochester

ISBN: (纸本)9781424439942

Associating image content with their geographic locations has been increasingly pursued in the computer vision community in recent years. In a recent work, large collections of geotagged images were found to be helpful in estimating geo-locations of query images by simple visual nearest-neighbors search. In this paper, we leverage user tags along with image content to infer the geo-location. Our model builds upon the fact that the visual content and user tags of pictures can provide significant hints about their geo-locations. Using a large collection of over a million geotagged photographs, we build location probability maps of user tags over the entire globe. These maps reflect the picture-taking and tagging behaviors of thousands of users from all over the world, and reveal interesting tag map patterns. Visual content matching is performed using multiple feature descriptors including tiny images, color histograms, GIST features, and bags of textons. The combination of visual content matching and local tag probability maps forms a strong geo-inference engine. Large-scale experiments have shown significant improvements over pure visual content-based geo-location inference.

关键词： Humans computer vision Search engines Large-scale systems Global Positioning System Image databases Laboratories Nearest neighbor searches Tagging Histograms

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Data handling displays

Data handling displays

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maxim Lazarov Hamed Pirsiavash Behzad Sajadi Uddipan Mukherjee Aditi Majumder University of California Irvine CA USA

Imagine a world in which people can use their hand-held mobile devices to project and transfer content. Everyone can join the collaboration by simply bringing their mobile devices close to each other. People can grab data from each others' devices with simple hand gestures. Now imagine a large display created by tiling multiple displays where multiple users can interact with a large dynamically changing data set in a collocated, collaborative setting and the displays will take care of the data transfer and handling functions in a way that is transparent to the users. In this paper we present a novel data-handling display which works as not only a display device but also as an interaction and data transfer module. We propose simple gesture based solutions to transfer information between these data-handling modules. We achieve high scalability by presenting a fully distributed architecture in which each device is responsible for its own data and also communicates and collaborates with other devices. We also show the usefulness of our work in visualizing large datasets and at the same time allowing multiple users to interact with the data.

关键词： Data handling Collaborative work Cameras computer displays Collaboration Collaborative software Scalability Data visualization User interfaces Mobile handsets

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Stochastic representation and recognition of high-level group activities: Describing structural uncertainties in human activities

Stochastic representation and recognition of high-level grou...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： M. S. Ryoo J. K. Aggarwal USA Computer & Vision Research Center / Department of ECE University of Texas Austin USA

Summary form only given. A significant amount of computer vision research has addressed the recognition of human activities recently. Researchers are particularly successful in recognizing the activities of one individual or between two individuals, such as pushing and fighting. Notably, in our previous work, we have presented a representation syntax to describe high-level human-human interactions based on their sub-events, and proposed a hierarchical algorithm to recognize represented interactions probabilistically. Not only simple interactions such as punching, kicking, and shaking hands are recognized, but also recursive interactions like “fighting” between two persons are recognized with our previous framework. In this paper, we take our next evolutionary step in human activity recognition: recognition of group activities. Group activities are the activities that can be characterized by movements of members who belong to one or more conceptual groups. recognition of groups and their activities will make the detection of high-level events possible. Especially, when such events are semantically meaningful in terms of overall actions of multiple persons considered jointly but not when they are considered individually. Automated recognition of suspicious groups and their activities such as 'a group of thieves robbing the bank' are essential for the construction of high-level surveillance systems. The analysis of movements and plays in team sports also becomes possible with the group activity recognition system. The semantic understanding of military operations and joint works is another application of group activity recognition. This paper describes a stochastic methodology for the recognition of various types of high-level group activities. Our system maintains the probabilistic representation of a group activity, describing how individual activities of its group members must be organized temporally, spatially, and logically. In order to recognize each of the repre

关键词： Stochastic processes Uncertainty Humans Videos computer vision Punching Event detection Surveillance Monte Carlo methods Probability distribution

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Multiple label prediction for image annotation with multiple Kernel correlation models

Multiple label prediction for image annotation with multiple...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Oksana Yakhnenko Vasant Honavar Computer Science Department Iowa State University USA

Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of an image into some semantic space, and then recovers the caption by performing another linear transformation from the semantic space into the label space. The model is trained so that model parameters minimize the error of reconstruction directly. This model is related to Canonical Correlation Analysis (CCA) which maps both images and caption into the semantic space to minimize the distance of mapping in the semantic space. Kernel trick is then used for the MLR resulting in Kernel Multiple Linear Regression model. The solution to KMLR is a solution to the generalized eigen-value problem, related to KCCA (Kernel Canonical Correlation Analysis). We then extend Kernel Multiple Linear Regression and Kernel Canonical Correlation analysis models to multiple kernel setting, to allow various representations of images and captions. We present results for image annotation using Multiple Kernel Learning CCA and MLR on Oliva and Torralba (2001) scene recognition that show kernel selection behaviour.

关键词： Kernel Predictive models Linear regression Image retrieval Image analysis Layout Image reconstruction Image representation Image sampling computer science

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On conversion from color to gray-scale images for face detection

On conversion from color to gray-scale images for face detec...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Juwei Lu K.N. Plataniotis Vidient Systems Inc. Santa Clara CA USA The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Canada

The paper presents a study on color to gray image conversion from a novel point of view: face detection. To the best knowledge of the authors, research in such a specific topic has not been conducted before. Our work reveals that the standard NTSC conversion is not optimal for face detection tasks, although it may be the best for use to display pictures on monochrome televisions. It is further found experimentally with two AdaBoost-based face detection systems that the detect rates may vary up to 10% by simply changing the parameters of the RGB to Gray conversion. On the other hand, the change has little influence on the false positive rates. Compared to the standard NTSC conversion, the detect rate with the best found parameter setting is 2.85% and 3.58% higher for the two evaluated face detection systems. Promisingly, the work suggests a new solution to the color to gray conversion. It could be extremely easy to be incorporated into most existing face detection systems for accuracy improvement without introduction of any extra cost in computational complexity.

关键词： Image converters Gray-scale Face detection Displays TV Color Computational efficiency Computational complexity Strontium Histograms

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Quasiconvex Alignment of Multimodal Skin Images for Quantitative Dermatology

Quasiconvex Alignment of Multimodal Skin Images for Quantita...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Siddharth K. Madan Kristin J. Dana Oana G. Cula Rutgers University Johnson & Johnson

ISBN: (纸本)9781424439942

In quantitative dermatology, high resolution sensors provide images that capture fine scale features like pores, birthmarks, and moles. Breathing and minute movements result in misregistration of micro level features. Many computer vision methods for dermatology such as change detection, appearance capture, and multi sensor fusion require high accuracy point-wise registration of micro level features. However, most computer vision algorithms are based on macro level features such as eyes, nose, and lips, and aren't suitable for registering micro level features. In this paper, we develop a practical robust algorithm to align face regions using skin texture with mostly indistinct micro level features. In computer vision, these regions would typically be considered featureless regions. Our method approximates the face surface as a collection of quasi-planar skin patches and uses quasiconvex optimization and the L{sub}∞ norm for estimation of spatially varying homographies. We have assembled a unique dataset of high resolution dermatology images comprised of over 100 human subjects. The image pairs vary in imaging modality (crossed, parallel and no polarization) and are misregistered due to the natural non-rigid human movement between image capture. This method of polarization based image capture is commonly used in dermatology to image surface and subsurface structure. Using this dataset, we show high quality alignment of "featureless" regions and demonstrate that the algorithm works robustly over a large set of subjects with different skin texture appearance, not just a few test images.

关键词： Skin computer vision Image resolution Change detection algorithms Robustness Face detection Polarization Image sensors Sensor phenomena and characterization Sensor fusion

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A case for the average-half-face in 2D and 3D for face recognition

A case for the average-half-face in 2D and 3D for face recog...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Josh Harguess J. K. Aggarwal Computer & Vision Research Center/ Department of ECE University of Texas Austin USA

We observe that the human face is inherently symmetric and we would like to exploit this symmetry in face recognition. The average-half-face has been previously shown to do just that for a set of 3D faces when using eigenfaces for recognition. We build upon that work and present a comparison of the use of the average-half-face to the use of the original full face with 6 different algorithms applied to two- and three-dimensional (2D and 3D) databases. The average-half-face is constructed from the full frontal face image in two steps; first the face image is centered and divided in half and then the two halves are averaged together (reversing the columns of one of the halves). The resulting average-half-face is then used as the input for face recognition algorithms. Previous work has shown that the accuracy of 3D face recognition using eigenfaces with the average-half-face is significantly better than using the full face. We compare the results using the average-half-face and the full face using six face recognition methods; eigenfaces, multi-linear principal components analysis (MPCA), MPCA with linear discriminant analysis (MPCALDA), Fisherfaces (LDA), independent component analysis (ICA), and support vector machines (SVM). We utilize two well-known 2D face database as well as a 3D face database for the comparison. Our results show that in most cases it is superior to employ the average-half-face for frontal face recognition. The consequences of this discovery may result in substantial savings in storage and computation time.

关键词： Face recognition Principal component analysis Linear discriminant analysis Independent component analysis Support vector machines Image databases Biometrics Scattering computer vision Humans

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Recursive compositional models: Representation, learning, and inference

Recursive compositional models: Representation, learning, an...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Long Zhu Alan Yuille CSAIL-MIT USA Department Statistics Psychology and Computer Science UCLA USA

Recursive compositional models (RCMs) are hierarchical models which enable us to represent the shape/geometry and visual appearance of objects and images at different scales. The key design principle is recursive compositionality. Objects are represented by RCMs in a hierarchical form where complex structures are composed of more elementary structures. Formally, they are represented by probability distributions defined over graphs with variable topology. Learning techniques are used to learn these models from a limited number of examples of the object by exploiting the recursive structure (some of our papers use supervised learning while others are unsupervised and induce the object structure). In addition, we can exploit this structure to develop algorithms that can perform inference on these RCMs to rapidly detect and recognize objects. This differs from more standard “flat models” of objects which have much less representational power if they wish to maintain efficient learning and inference. The basic properties of an RCM are illustrated in figures (1, 2). Because RCMs give a rich hierarchical description of objects and images they can be applied to a range of tasks including object detection, segmentation, parsing and image parsing. In all cases, we achieved state of the art results when evaluated on datasets with groundtruth.

关键词： Topology Solid modeling Shape Object detection Statistics Psychology computer science Computational geometry Information geometry Probability distribution

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Categorization in natural time-varying image sequences

Categorization in natural time-varying image sequences

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Teresa Ko Stefano Soatto Deborah Estrin Vision Laboratory University of California Los Angeles Los Angeles CA USA

Approaches to single image categorization do not easily generalize to natural time-varying image sequences. In natural environments, object categories tend to have few features that help to distinguish between each other and the surrounding environment. To better discriminate between categories and the surrounding environment, we propose a multi-view categorization approach that exploits the statistics of image sequences rather than single images. The approach is unbiased towards redundant views - that is, it does not matter how many times an object appears from the same viewpoint. At the same time, the approach does not penalize for missing views, so that we do not have to capture an object at all viewpoints to successfully categorize the object. We first present a data set for studying natural environment monitoring: an image sequence of birds at a feeder station. After manual localization, a baseline bag of features approach was found to perform significantly worse on the proposed data set compared to the standard Caltech 101 data set. We find that our approach increases the categorization accuracy from 48% to 58% on average when compared to an equivalent single view categorization method. Finally, we show how the same metric proposed for the supervised categorization can be used to transform, in an unsupervised manner, an image sequence into a manageable set of categories.

关键词： Image sequences Monitoring Birds Surveillance Histograms Face detection Spatial resolution Testing Statistics Costs

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Groupwise Morphometric Analysis based on Morphological Appearance Manifold

Groupwise Morphometric Analysis based on Morphological Appea...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Nai-Xiang Lian Christos Davatzikos Department of Radiology University of Pennsylvania

ISBN: (纸本)9781424439942

The field of computational anatomy has developed rigorous frameworks for analyzing anatomical shape, based on diffeomorphic transformations of a template. However, differences in algorithms used for template warping, in regularization parameters, and in the template itself, lead to different representations of the same anatomy. Variations of these parameters are considered as confounding factors as they give rise to non-unique representation. Recently, extensions of the conventional computational anatomy framework to account for such confounding variations have shown that learning the equivalence class derived from the multitude of representations can lead to improved and more stable morphological descriptors. Herein, we follow that approach, estimating the morphological appearance manifold obtained by varying parameters of the template warping procedure. Our approach parallels work in the computer vision field, in which variations lighting, pose and other parameters leads to image appearance manifolds representing the exact same figure in different ways. The proposed framework is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion on selected complete morphological descriptor to perform manifold-constrained optimization, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations reflecting purely biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local approximations of the manifold via PCA.

关键词： Anatomy Shape Biological system modeling Principal component analysis Biology computing Biomedical measurements Smoothing methods Radiology computer vision Statistical analysis

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