OpenRaiser/Intern-Atlas huggingface dataset:
Conformal embedding analysis with local graph modeling on the unit hypersphere
Authors: Yun Fu 0001, Ming Liu 0009, Thomas S. Huang
Venue: Conference CVPR 2007
Year: 2007

We present the Conformal Embedding Analysis (CEA) for feature extraction and dimensionality reduction. Incorporating both conformal mapping and discriminating analysis, CEA projects the high-dimensional data onto the unit hypersphere and preserves intrinsic neighbor relations with local graph modeling. Through the embedding, resulting data pairs from the same class keep the original angle and distance information on the hypersphere, whereas neighboring points of different class are kept apart to boost discriminating power. The subspace learned by CEA is gray-level variation tolerable since the cosine-angle metric and the normalization processing enhance the robustness of the conformal feature extraction. We demonstrate the effectiveness of the proposed method with comprehensive comparisons on visual classification experiments.
Shape from Varying Illumination and Viewpoint
Authors: Neel Joshi, David J. Kriegman
Venue: Conference ICCV 2007
Year: 2007

We address the problem of reconstructing the 3-D shape of a Lambertian surface from multiple images acquired as an object rotates under distant and possibly varying illumination. Using camera projection matrices estimated from point correspondences across views, the algorithm computes a dense correspondence map by minimizing a multi-ocular photometric constraint. Once correspondence across views is established, photometric stereo is applied to estimate a surface normal field and 3-D surface. Conceptually, the algorithm merges multi-view stereo and photometric stereo and uses aspects of both methods to recover shape. The method is straightforward to implement and relies on established principles from the two stereo methods. We empirically validate the method on images of a number of objects and show that it outperforms previous methods.
Intra- and inter-rater agreement in a subjective speech quality assessment task in crowdsourcing
Authors: Rafael Zequeira Jiménez, Anna Llagostera, Babak Naderi, Sebastian Möller, Jens Berger
Venue: Conference WWW 2019
Year: 2019

Crowdsourcing is a great tool for conducting subjective user studies with large amounts of users. Collecting reliable annotations about the quality of speech stimuli is challenging. The task itself is of high subjectivity and users in crowdsourcing work without supervision. This work investigates the intra- and inter-listener agreement withing a subjective speech quality assessment task. To this end, a study has been conducted in the laboratory and in crowdsourcing in which listeners were requested to rate speech stimuli with respect to their overall quality. Ratings were collected on a 5-point scale in accordance with the ITU-T Rec. P.800 and P.808, respectively. The speech samples were taken from the database ITU-T Rec. P.501 Annex D, and were presented four times to the listeners. Finally, the crowdsourcing results were contrasted to the ratings collected in the laboratory. Strong and significant Spearman’s correlation was achieved when contrasting the ratings collected in both environments. Our analysis show that while the inter-rater agreement increased the more the listeners conducted the assessment task, the intra-rater reliability remained constant. Our study setup helped to overcome the subjectivity of the task and we found that disagreement can represent a source of information to some extent.
Depth measurement by the multi-focus camera
Authors: Shinsaku Hiura, Takashi Matsuyama
Venue: Conference CVPR 1998
Year: 1998

In this paper, we first introduce the multi-focus camera, a new image sensor used for depth from defocus (DFD) range measurement. It can capture three images with different focus values simultaneously. We then propose two different depth measurement methods using the camera. The first method, an augmented version of the one proposed by N. Asada et al. (1998), employs a noniterative optimization process to compute depth values on edge points. The second one incorporates a coded aperture with the camera; and applies model-based pattern matching to estimate depth values of textured surfaces. Here we propose two types of coded apertures and corresponding analysis algorithms: 1D Fourier analysis to acquire a depth map and a blur-free image from three defocused images taken with a pair of pinholes, and 2D convolution based model matching for the fast and precise depth measurement using a coded aperture with four pinholes. Experimental results showed that the multi-focus camera works well as a practical DFD range sensor and that the coded apertures much improve its range estimation capability for real world scenes.
Projective registration with difference decomposition
Authors: Michael Gleicher
Venue: Conference CVPR 1997
Year: 1997

Current methods for registering image regions perform well for simple transformations or large image regions. The author presents a new method that is better able to handle small image regions as they deform with nonlinear transformations. He introduces difference decomposition, a novel approach to solving the registration problem. The method is a generalization of previous methods and can better handle nonlinear transforms. Although the methods are general, he focuses on projective transformations and introduces piecewise-projective transformations for modeling the motions of non-planar objects. He concludes with examples from a prototype implementation.
Qualitative Detection of Motion by a Moving Observer
Authors: Randal C. Nelson
Venue: Conference CVPR 1991
Year: 1991

Two complementary methods for the detection of moving objects by a moving observer are described. The first (constraint ray filtering) uses a constraint that restricts the projected velocity at any image point to a 1-D locus in velocity space to detect motion inconsistent with a rigid world assumption. The second (animate motion detection) utilizes a constraint on the time-rate-of-change of projected velocity due to smooth observer motion to detect moving objects such as animals and maneuvering vehicles whose projected motion changes rapidly. In both cases, the qualitative nature of the constraints allows the methods to be used with the inexact motion information typically available from real image sequences. Implementations of the methods that run in real time on a parallel pipelined image processing system are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Probabilistic temporal inference on reconstructed 3d scenes
Authors: Grant Schindler, Frank Dellaert
Venue: Conference CVPR 2010
Year: 2010

Modern structure from motion techniques are capable of building city-scale 3D reconstructions from large image collections, but have mostly ignored the problem of large-scale structural changes over time. We present a general framework for estimating temporal variables in structure from motion problems, including an unknown date for each camera and an unknown time interval for each structural element. Given a collection of images with mostly unknown or uncertain dates, we use this framework to automatically recover the dates of all images by reasoning probabilistically about the visibility and existence of objects in the scene. We present results on a collection of over 100 historical images of a city taken over decades of time.
Tracking self-occluding articulated objects in dense disparity maps
Authors: Nebojsa Jojic, Matthew Turk 0001, Thomas S. Huang
Venue: Conference ICCV 1999
Year: 1999

In this paper, we present an algorithm for real-time tracking of articulated structures in dense disparity maps derived from stereo image sequences. A statistical image formation model that accounts for occlusions plays the central role in our tracking approach. This graphical model (a Bayesian network) assumes that the range image of each part of the structure is formed by drawing the depth candidates from a 3-D Gaussian distribution. The advantage over the classical mixture of Gaussians is that our model takes into account occlusions by picking the minimum depth (which could be regarded as a probabilistic version of z-buffering). The model also enforces articulation constraints among the parts of the structure. The tracking problem is formulated as an inference problem in the image formation model. This model can be extended and used for other tasks in addition to the one described in the paper and can also be used for estimating probability distribution functions instead of the ML estimates of the tracked parameters. For the purposes of real-time tracking, we used certain approximations in the inference process, which resulted in a real-time two-stage inference algorithm. We were able to successfully track upper human body motion in real time and in the presence of self-occlusions.
Game-Theoretic Multiple Target Tracking
Authors: Ming Yang 0007, Ting Yu 0003, Ying Wu 0001
Venue: Conference ICCV 2007
Year: 2007

Video-based multiple target tracking (MTT) is a challenging task when similar targets are present in close vicinity. Because their visual observations are mixed and difficult to segment, their motions have to be estimated jointly. Most existing approaches perform this joint motion estimation in a centralized fashion and involve searching a rather high dimensional space, and thus leading to quite complicated joint trackers. This paper brings a new view to MTT from a game-theoretic perspective, bridging the joint motion estimation and the Nash equilibrium of a game. Instead of designing a centralized tracker, MTT is decentralized and a set of individual trackers is used, each of which tries to maximize its visual evidence for explaining its motion as well as generates interferences to others. Modelling this competition behavior, a special game is designed so that the difficult joint motion estimation is achieved at the Nash Equilibrium of this game where no individual tracker has incentives to change its motion estimate. This paper substantializes this novel idea in a solid case study where individual trackers are kernel-based trackers. An efficient best response updating procedure is designed to find the Nash equilibrium. The powerfulness of this game-theoretic MTT is shown by promising results on difficult real videos.
An evaluation of parallel eccentricity estimation algorithms on undirected real-world graphs
Authors: Julian Shun
Venue: Conference KDD 2015
Year: 2015

This paper presents efficient shared-memory parallel implementations and the first comprehensive experimental study of graph eccentricity estimation algorithms in the literature. The implementations include (1) a simple algorithm based on executing two-pass breadth-first searches from a sample of vertices, (2) algorithms with sub-quadratic worst-case running time for sparse graphs and non-trivial approximation guarantees that execute breadth-first searches from a carefully chosen set of vertices, (3) algorithms based on probabilistic counters, and (4) a well-known 2-approximation algorithm that executes one breadth-first search per connected component. Our experiments on large undirected real-world graphs show that the algorithm based on two-pass breadth-first searches works surprisingly well, outperforming the other algorithms in terms of running time and/or accuracy by up to orders of magnitude. The high accuracy, efficiency, and parallelism of our best implementation allows the fast generation of eccentricity estimates for large graphs, which are useful in many applications arising in large-scale network analysis.
Clova: Services and devices powered by AI
Authors: Inho Kang
Venue: Conference SIGIR 2018
Year: 2018

NAVER, Korea's No. 1 internet company, and LINE, the Japan-based global messenger platform, have teamed up to use their extensive online content databases (documents, maps, encyclopedia, etc.) and user information to develop an AI platform called Clova (Cloud-based Virtual Assistant). Clova's deep-learning based methods have made tremendous progress in image classification and intelligent Q&A response. For example, in Naver, users can procure product or location information by showing pictures. Keyword-based matching is enhanced using semantic analysis, and retrieving top links is changed to answering questions by refining user queries through dialogue. In addition to PC and mobile, Clova is evolving to enable a user to access the relevant information using multiple devices and interfaces. Line released a smart speaker called Clova Friends that includes Line's free voice-call function and infrared home-appliance control. IPTV set-top box powered-by the Clova AI platform which can recommend movies based on Naver's user reviews. In this talk, I will cover some efforts and challenges in understanding and satisfying users on each device with sophisticated natural language processing technologies. I will introduce the technically challenging problems that we are currently tackling and future AI developments.
Surface Reconstruction: GNCs and MFA
Authors: Mads Nielsen
Venue: Conference ICCV 1995
Year: 1995

The reconstruction of noise corrupted surfaces can be inferred by methodologies such as Bayesian estimation and minimum description length. Both of these imply a formulation where the reconstruction minimizes a functional. Often this functional is non convex and the minimum cannot be found by simple gradient methods. The paper concerns functionals with quadratic data term, criteria for such functionals to be convex, and the variational approach of minimizing non convex functionals. Initial convexity of the approximating functional is considered to be a critical point. Two fully automatic methods of generating convex functionals are presented. They are based on Gaussian convolution and are compared to the Blake-Zisserman graduated non convexity (GNC) (A. Blake, A. Zisserman, 1987) and G.L. Bilbro et al. (1992) and D. Geiger and F. Girosi's (1991) mean field annealing (MFA) of the weak membrane.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Generating semantic trajectories using a car signal ontology
Authors: Benjamin Klotz, Raphaël Troncy, Daniel Wilms, Christian Bonnet
Venue: Conference WWW 2018
Year: 2018

In this paper, we use semantic technologies for enriching trajectory data in the automotive industry for offline analysis. We proposed to re-use a combination of existing ontologies and we designed a Vehicle Signal Specification ontology to provide an environment in which we developed an application that analyzes the variations of signal values and enables to infer the "driving smoothness'' that we represent as additional annotations of semantic trajectories.
Mining the most interesting rules
Authors: Roberto J. Bayardo Jr., Rakesh Agrawal
Venue: Conference KDD 1999
Year: 1999

Several algorithms have been proposed for finding the “best, ” “optimal,” or “most interesting ” rule(s) in a database according to a variety of metrics including confidence, support, gain, chi-squared value, gini, entropy gain, laplace, lift, and conviction. In this paper, we show that the best rule according to any of these metrics must reside along a support/confidence border. Further, in the case of conjunctive rule mining within categorical data, the number of rules along this border is conveniently small, and can be mined efficiently from a variety of real-world data-sets. We also show how this concept can be generalized to mine all rules that are best according to any of these criteria with respect to an arbitrary subset of the population of interest. We argue that by returning a broader set of rules than previous algorithms, our techniques allow for improved insight into the data and support more user-interaction in the optimized rule-mining process. 1.
Predictive modeling for public health: Preventing childhood lead poisoning
Authors: Eric Potash, Joe Brew, Alexander Loewi, Subhabrata Majumdar, Andrew Reece, Joe Walsh, Eric Rozier, Emile Jorgenson, Raed Mansour, Rayid Ghani
Venue: Conference KDD 2015
Year: 2015

Lead poisoning is a major public health problem that affects hundreds of thousands of children in the United States every year. A common approach to identifying lead hazards is to test all children for elevated blood lead levels and then investigate and remediate the homes of children with elevated tests. This can prevent exposure to lead of future residents, but only after a child has been poisoned. This paper describes joint work with the Chicago Department of Public Health (CDPH) in which we build a model that predicts the risk of a child to being poisoned so that an intervention can take place before that happens. Using two decades of blood lead level tests, home lead inspections, property value assessments, and census data, our model allows inspectors to prioritize houses on an intractably long list of potential hazards and identify children who are at the highest risk. This work has been described by CDPH as pioneering in the use of machine learning and predictive analytics in public health and has the potential to have a significant impact on both health and economic outcomes for communities across the US.
Topical trustrank: using topicality to combat web spam
Authors: Baoning Wu, Vinay Goel, Brian D. Davison
Venue: Conference WWW 2006
Year: 2006

Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the sense that the seed set used by TrustRank may not be sufficiently representative to cover well the different topics on the Web. Also, for a given seed set, TrustRank has a bias towards larger communities. We propose the use of topical information to partition the seed set and calculate trust scores for each topic separately to address the above issues. A combination of these trust scores for a page is used to determine its ranking. Experimental results on two large datasets show that our Topical TrustRank has a better performance than TrustRank in demoting spam sites or pages. Compared to TrustRank, our best technique can decrease spam from the top ranked sites by as much as 43.1%.
Exploiting asymmetry in hierarchical topic extraction
Authors: Sreenivas Gollapudi, Rina Panigrahy
Venue: Conference CIKM 2006
Year: 2006

Topic or feature extraction is often used as an important step in document classification and text mining. Topics are succinct representation of content in a document collection and hence are very effective when used as content identifiers in peer-to-peer systems and other large scale distributed content management systems. Effective topic extraction is dependent on the accuracy of term clustering that often has to deal with problems like synonymy and polysemy. Retrieval techniques based on spectral analysis like Latent Semantic Indexing (LSI) are often used to effectively solve these problems. Most of the spectral retrieval schemes produce term similarity measures that are symmetric and often, not an accurate characterization of term relationships. Another drawback of LSI is its running time that is polynomial in the dimensions of the m x n matrix, A. This can get prohibitively large for some IR applications. In this paper, we present efficient algorithms using the technique of Locality-Sensitive Hashing (LSH) to extract topics from a document collection based on the asymmetric relationships between terms in a collection. The relationship is characterized by the term co-occurrences and other higher-order similarity measures. Our LSH based scheme can be viewed as a simple alternative to LSI. We show the efficacy of our algorithms via experiments on a set of large documents. An interesting feature of our algorithms is that it produces a natural hierarchical decomposition of the topic space instead of a flat clustering.
Fast mining of spatial collocations
Authors: Xin Zhang, Nikos Mamoulis, David W. Cheung, Yutao Shou
Venue: Conference KDD 2004
Year: 2004

Spatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.
Efficient Decision Tree Construction on Streaming Data
Authors: Ruoming Jin, Gagan Agrawal
Venue: Conference KDD 2003
Year: 2003

Decision tree construction is a well studied problem in data mining. Recently, there has been much interest in mining streaming data. Domingos and Hulten have presented a one-pass algorithm for decision tree construction. Their work uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed.In this paper, we revisit this problem. We make the following two contributions: 1) We present a numerical interval pruning (NIP) approach for efficiently processing numerical attributes. Our results show an average of 39% reduction in execution times. 2) We exploit the properties of the gain function entropy (and gini) to reduce the sample size required for obtaining a given bound on the accuracy. Our experimental results show a 37% reduction in the number of data instances required.
Who should share what?: item-level social influence prediction for users and posts ranking
Authors: Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang, Lifeng Sun
Venue: Conference SIGIR 2011
Year: 2011

People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an item, who should share it so that its diffusion range can be maximized in a social network; (2) Web posts ranking: given a user, what should she share to maximize her influence among her friends. We formulate the social influence prediction problem as the estimation of a user-post matrix, in which each entry represents the strength of influence of a user given a web post. We propose a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF-NMF problem. Intensive experiments are conducted and demonstrate the advantages and characteristics of the proposed method.
Action classification on product manifolds
Authors: Yui Man Lui, J. Ross Beveridge, Michael Kirby
Venue: Conference CVPR 2010
Year: 2010

Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property for action classification. We characterize a tensor as a point on a product manifold and perform classification on this space. First, we factorize a tensor relating to each order using a modified High Order Singular Value Decomposition (HOSVD). We recognize each factorized space as a Grassmann manifold. Consequently, a tensor is mapped to a point on a product manifold and the geodesic distance on a product manifold is computed for tensor classification. We assess the proposed method using two public video databases, namely Cambridge-Gesture gesture and KTH human action data sets. Experimental results reveal that the proposed method performs very well on these data sets. In addition, our method is generic in the sense that no prior training is needed.