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Insights from applying sequential pattern mining to e-commerce click stream data

Insights from applying sequential pattern mining to e-commer...

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10th ieee International conference on Data Mining workshops, ICDMW 2010

作者： Pitman, Arthur Zanker, Markus Alpen-Adria-Universität Klagenfurt Klagenfurt Austria

ISBN: (纸本)9780769542577

Previous sequential pattern mining algorithms have focused on improving performance in terms of runtime and memory consumption without considering the specifics of different data sources or application scenarios. In this paper, we focus on mining closed sequential patterns from website click streams by extending the state of the art Bi-Directional Extension (BIDE) algorithm in order to identify domain-specific rule sets. In particular, we focus on exploiting sequential patterns for landing page personalization and product recommendation in the e-commerce domain. Our contribution is therefore of algorithmic as well as of empirical nature. Based on a dataset that we derived from an online store for nutritional supplements, we evaluate the effectiveness of using different sources of domain knowledge, such as product hierarchies and search word categorizations, to enhance predictions about the conversion actions of users. Furthermore, we examine the performance of the recommender for two important user subgroups, namely those that use search functionality and those that don't. Our findings indicate for instance that search terms alone are already quite effective for predicting users' add-to-basket actions and that using additional domain knowledge to generate multi-dimensional rules does not always lead to improved accuracy. © 2010 ieee.

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Optimizing kd-trees for scalable visual descriptor indexing

Optimizing kd-trees for scalable visual descriptor indexing

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conference on computer vision and pattern recognition (CVPR)

作者： You Jia Jingdong Wang Gang Zeng Hongbin Zha Xian-Sheng Hua Key Laboratory of Machine Perception Peking University China Microsoft Research Asia China

In this paper, we attempt to scale up the kd-tree indexing methods for large-scale vision applications, e.g., indexing a large number of SIFT features and other types of visual descriptors. To this end, we propose an effective approach to generate near-optimal binary space partitioning and need low time cost to access the nodes in the query stage. First, we relax the coordinate-axis-alignment constraint in partition axis selection used in conventional kd-trees, and form a partition axis with the great variance by combining a few coordinate axes in a binary manner for each node, which yields better space partitioning and requires almost the same time cost to visit internal nodes during the query stage thanks to cheap projection operations. Then, we introduce a simple but very effective scheme to guarantee the partition axis of each internal node is orthogonal to or parallel with those of its ancestors, which leads to efficient distance computation between a query point and the cell associated with each node and yields fast priority search. Compared with the conventional kd-trees, our approach takes a little more tree construction time, but obtains much better nearest neighbor search performance. Experimental results on large scale local patch indexing and image search with tiny images show that our approach outperforms the state-of-the-art kd-tree based indexing methods.

关键词： Indexing Nearest neighbor searches Neural networks Large-scale systems Costs Application software Image databases Spatial databases Asia computer vision

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Unsupervised learning of invariant features using video

Unsupervised learning of invariant features using video

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conference on computer vision and pattern recognition (CVPR)

作者： David Stavens Sebastian Thrun Computer Science Department Stanford Artificial Intelligence Laboratory Stanford CA USA

We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without human intervention to a particular application or data set, learning the specific invariances necessary for excellent feature performance on that data. Our algorithm relies on the ability to track image patches over time using optical flow. With the wide availability of high frame rate video (eg: on the web, from a robot), good tracking is straightforward to achieve. The algorithm then optimizes feature parameters such that patches corresponding to the same physical location have feature descriptors that are as similar as possible while simultaneously maximizing the distinctness of descriptors for different locations. Thus, our method captures data or application specific invariances yet does not require any manual supervision. We apply our algorithm to learn domain-optimized versions of SIFT and HOG. SIFT and HOG features are excellent and widely used. However, they are general and by definition not tailored to a specific domain. Our domain-optimized versions offer a substantial performance increase for classification and correspondence tasks we consider. Furthermore, we show that the features our method learns are near the optimal that would be achieved by directly optimizing the test set performance of a classifier. Finally, we demonstrate that the learning often allows fewer features to be used for some tasks, which has the potential to dramatically improve computational concerns for very large data sets.

关键词： Unsupervised learning Testing Surgery Image motion analysis Optimization methods Machine learning algorithms Streaming media Artificial intelligence Laboratories computer science

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Total Bregman divergence and its applications to shape retrieval

Total Bregman divergence and its applications to shape retri...

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conference on computer vision and pattern recognition (CVPR)

作者： Meizhu Liu Baba C. Vemuri Shun-Ichi Amari Frank Nielsen CISE University of Florida USA RIKEN Brain Science Institute Japan Sony Computer Science Laboratories Inc.

Shape database search is ubiquitous in the world of bio-metric systems, CAD systems etc. Shape data in these domains is experiencing an explosive growth and usually requires search of whole shape databases to retrieve the best matches with accuracy and efficiency for a variety of tasks. In this paper, we present a novel divergence measure between any two given points in R n or two distribution functions. This divergence measures the orthogonal distance between the tangent to the convex function (used in the definition of the divergence) at one of its input arguments and its second argument. This is in contrast to the ordinate distance taken in the usual definition of the Bregman class of divergences. We use this orthogonal distance to redefine the Bregman class of divergences and develop a new theory for estimating the center of a set of vectors as well as probability distribution functions. The new class of divergences are dubbed the total Bregman divergence (TBD). We present the l\-norm based TBD center that is dubbed the t-center which is then used as a cluster center of a class of shapes The t-center is weighted mean and this weight is small for noise and outliers. We present a shape retrieval scheme using TBD and the t-center for representing the classes of shapes from the MPEG-7 database and compare the results with other state-of-the-art methods in literature.

关键词： Shape Information retrieval Databases Application software computer science Biometrics Explosives Distribution functions Estimation theory Probability distribution

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Compression of surface registrations using Beltrami coefficients

Compression of surface registrations using Beltrami coeffici...

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conference on computer vision and pattern recognition (CVPR)

作者： Lok Ming Lui Tsz Wai Wong Paul Thompson Tony Chan Xianfeng Gu Shing-Tung Yau Department of Mathematics UCLA Los Angeles CA USA Department of Mathematics Harvard University Cambridge MA USA Laboratory of Neuro Imaging School of Medicine UCLA Los Angeles CA USA Hong Kong University of Science and Technology Hong Kong China Department of Computer Science SUNY Stony Brook Stony Brook NY USA

Surface registration is widely used in machine vision and medical imaging, where 1-1 correspondences between surfaces are computed to study their variations. Surface maps are usually stored as the 3D coordinates each vertex is mapped to, which often requires lots of storage memory. This causes inconvenience in data transmission and data storage, especially when a large set of surfaces are analyzed. To tackle this problem, we propose a novel representation of surface diffeomorphisms using Beltrami coefficients, which are complex-valued functions defined on surfaces with supreme norm less than 1. Fixing any 3 points on a pair of surfaces, there is a 1-1 correspondence between the set of surface diffeomorphisms between them and the set of Beltrami coefficients on the source domain. Hence, every bijective surface map can be represented by a unique Bel-trami coefficient. Conversely, given a Beltrami coefficient, we can reconstruct the unique surface map associated to it using the Beltrami Holomorphic flow (BHF) method introduced in this paper. Using this representation, 1/3 of the storage space is saved. We can further reduce the storage requirement by 90% by compressing the Beltrami coefficients using Fourier approximations. We test our algorithm on synthetic data, real human brain and hippocampal surfaces. Our results show high accuracy in the reconstructed data, while the amount of storage is greatly reduced. Our approach is compared with the Fourier compression of the coordinate functions using the same amount of data. The latter approach often shows jaggy results and cannot guarantee to preserve diffeomorphisms.

关键词： Surface reconstruction Biomedical imaging Image reconstruction Shape Mathematics computer vision Data communication Laboratories Neuroimaging computer science

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Accurate 3D face reconstruction from weakly calibrated wide baseline images with profile contours

Accurate 3D face reconstruction from weakly calibrated wide ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuping Lin Gérard Medioni Jongmoo Choi Computer Science Department University of Southern California Los Angeles CA USA

We propose a method to generate a highly accurate 3D face model from a set of wide-baseline images in a weakly calibrated setup. Our approach is purely data driven, and produces faithful 3D models without any pre-defined models, unlike other statistical model-based approaches. Our results do not rely upon a critical initialization step nor parameters for optimization steps. We process 5 images (including profile views), infer the accurate poses of cameras in all views, and then infer a dense 3D face model. The quality of 3D face models depends on the accuracy of estimated head-camera motion. First, we propose to use an iterative bundle adjustment approach to remove outliers in corresponding points. Contours in the profile views are matched to provide reliable correspondences that link two opposite side of views together. For dense reconstruction, we propose to use a face-specific cylindrical representation which allows us to solve a global optimization problem for N-view dense aggregation. Profile contours are used once again to provide constraints in the optimization step. Experimental results using synthetic and real images show that our method provides accurate and stable reconstruction results on wide-baseline images. We compare our method with state of the art methods, and show that it provides significantly better results in terms of both accuracy and efficiency.

关键词： Image reconstruction Cameras Motion estimation Head Shape Iterative methods Surface reconstruction Calibration Clouds computer science

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Multi-task warped Gaussian process for personalized age estimation

Multi-task warped Gaussian process for personalized age esti...

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conference on computer vision and pattern recognition (CVPR)

作者： Yu Zhang Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Automatic age estimation from facial images has aroused research interests in recent years due to its promising potential for some computer vision applications. Among the methods proposed to date, personalized age estimation methods generally outperform global age estimation methods by learning a separate age estimator for each person in the training data set. However, since typical age databases only contain very limited training data for each person, training a separate age estimator using only training data for that person runs a high risk of overfitting the data and hence the prediction performance is limited. In this paper, we propose a novel approach to age estimation by formulating the problem as a multi-task learning problem. Based on a variant of the Gaussian process (GP) called warped Gaussian process (WGP), we propose a multi-task extension called multi-task warped Gaussian process (MTWGP). Age estimation is formulated as a multi-task regression problem in which each learning task refers to estimation of the age function for each person. While MTWGP models common features shared by different tasks (persons), it also allows task-specific (person-specific) features to be learned automatically. Moreover, unlike previous age estimation methods which need to specify the form of the regression functions or determine many parameters in the functions using inefficient methods such as cross validation, the form of the regression functions in MTWGP is implicitly defined by the kernel function and all its model parameters can be learned from data automatically. We have conducted experiments on two publicly available age databases, FG-NET and MORPH. The experimental results are very promising in showing that MTWGP compares favorably with state-of-the-art age estimation methods.

关键词： Gaussian processes Aging Training data Face detection Face recognition Feature extraction Databases Kernel State estimation Pediatrics

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Beyond active noun tagging: Modeling contextual interactions for multi-class active learning

Beyond active noun tagging: Modeling contextual interactions...

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conference on computer vision and pattern recognition (CVPR)

作者： Behjat Siddiquie Abhinav Gupta Department of Computer Science University of Maryland College Park USA Robotics Institute Carnegie Mellon University USA

We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class active learning approaches have focused on utilizing classification uncertainty of regions to select the most ambiguous region for labeling. These approaches, however, ignore the contextual interactions between different regions of the image and the fact that knowing the label for one region provides information about the labels of other regions. For example, the knowledge of a region being sea is informative about regions satisfying the “on” relationship with respect to it, since they are highly likely to be boats. We explicitly model the contextual interactions between regions and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy). We also introduce a new methodology of posing labeling questions, mimicking the way humans actively learn about their environment. In these questions, we utilize the regions linked to a concept with high confidence as anchors, to pose questions about the uncertain regions. For example, if we can recognize water in an image then we can use the region associated with water as an anchor to pose questions such as “what is above water?”. Our active learning framework also introduces questions which help in actively learning contextual concepts. For example, our approach asks the annotator: “What is the relationship between boat and water?” and utilizes the answer to reduce the image entropies throughout the training dataset and obtain more relevant training examples for appearance models.

关键词： Tagging Context modeling Entropy Labeling Boats Humans Layout Uncertainty computer science Educational institutions

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Toward Coherent Object Detection And Scene Layout Understanding

Toward Coherent Object Detection And Scene Layout Understand...

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ieee conference on computer vision and pattern recognition

作者： Sid Ying-Ze Bao Min Sun Silvio Savarese Dept. of Electrical and Computer Engineering University of Michigan at Ann Arbor Dept. of Electrical and Computer Engineering University of Michigan at AnnArbor

ISBN: (纸本)9781424469840;9781424469857

Detecting objects in complex scenes while recovering the scene layout is a critical functionality in many vision-based applications. Inspired by the work of [18], we advocate the importance of geometric contextual reasoning for object recognition. We start from the intuition that objects' location and pose in the 3D space are not arbitrarily distributed but rather constrained by the fact that objects must lie on one or multiple supporting surfaces. We model such supporting surfaces by means of hidden parameters (i.e. not explicitly observed) and formulate the problem of joint scene reconstruction and object recognition as the one of finding the set of parameters that maximizes the joint probability of having a number of detected objects on K supporting planes given the observations. As a key ingredient for solving this optimization problem, we have demonstrated a novel relationship between object location and pose in the image, and the scene layout parameters (i.e. normal of one or more supporting planes in 3D and camera pose, location and focal length). Using the probabilistic formulation and the above relationship our method has the unique ability to jointly: i) reduce false alarm and false negative object detection rate;ii) recover object location and supporting planes within the 3D camera reference system;iii) infer camera parameters (view point and the focal length) from just one single uncalibrated image. Quantitative and qualitative experimental evaluation on a number of datasets (a novel in-house dataset and label-me[28] on car and pedestrian) demonstrates our theoretical claims.

关键词： FOCAL LENGTH object recognition Object detection layouts Cameras Joint Probability False alarms plane Intuition Pedestrians images Imagery (Psychotherapy) Cars

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Illumination compensation based change detection using order consistency

Illumination compensation based change detection using order...

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conference on computer vision and pattern recognition (CVPR)

作者： Vasu Parameswaran Maneesh Singh Visvanathan Ramesh Real Time Vision and Industrial Imaging Siemens Corporate Research Siemens Corporation Research Princeton NJ USA

We present a change detection method resistant to global and local illumination variations for use in visual surveillance scenarios. Approaches designed thus far for robustness to illumination change are generally based either on color normalization, texture (e.g. edges, rank order statistics, etc.), or illumination compensation. Normalization based methods sacrifice discriminability while texture based methods cannot operate on texture-less regions. Both types of method can produce large missing regions in the distance image which in turn pose problems for higher-level processing tasks that may be shape or region-based and require accurate foreground masks (e.g. person detection and tracking, crowd segmentation, etc.). Texture based methods have an additional problem in that they produce false alarms due to textures induced by local illumination effects (e.g. cast shadows). In this paper we propose a compensation based approach for change detection. Prior work on compensation has largely taken an empirical approach, and has not dealt with the important problem of rejecting outliers when they dominate the scene. In contrast, our generative approach and systematic handling of outliers enables us to achieve robustness to illumination change while eliminating the problems mentioned above. Furthermore, the computational complexity of our method is low enough for real-time performance. Results comparing images taken under strongly different illumination conditions, demonstrate the power and generality of the proposed method.

关键词： Lighting Robustness Layout Cameras Surveillance Image edge detection Statistics Shape Image segmentation Computational complexity

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