OpenRaiser/Intern-Atlas huggingface dataset:
Integrated person tracking using stereo, color, and pattern detection
Authors: Trevor Darrell, Gaile G. Gordon, Michael Harville, John Woodfill
Venue: Conference CVPR 1998
Year: 1998

We present an approach to real-time person tracking in crowded and/or unknown environments using multi-modal integration. We combine stereo, color and face detection modules into a single robust system, and show an initial application in an interactive, face-responsive display. Dense, real-time stereo processing is used to isolate users from other objects and people in the background. Skin-hue classification identifies and tracks likely body parts within the silhouette of a user. Face pattern detection discriminates and localizes the face within the identified body parts. Faces and bodies of users are tracked over several temporal scales: short-term (user stay's within the field of view), medium-term (user exits/reenters within minutes), and long term (user returns after hours or days). Short-term tracking is performed using simple region position and size correspondences, while medium and long-term tracking are based on statistics of user appearance. We discuss the failure modes of each individual module, describe our integration method, and report results with the complete system in trials with thousands of users.
Modeling interleaved hidden processes
Authors: Niels Landwehr
Venue: Conference ICML 2008
Year: 2008

Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by assuming that the observed data stems from multiple hidden processes, whose outputs interleave to form the sequence of observations. Exact inference in this model is NP-hard. However, a tractable and effective inference algorithm is obtained by extending structured approximate inference methods used in factorial hidden Markov models. The proposed model is evaluated in an activity recognition domain, where multiple activities interleave and together generate a stream of sensor observations. It is shown to be more accurate than a standard hidden Markov model in this domain.
Fully automatic registration of 3d point clouds
Authors: Ameesh Makadia, Alexander Patterson, Kostas Daniilidis
Venue: Conference CVPR 2006
Year: 2006

We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaussian Images even when the data sets inducing them have partial overlap. The technique is based on the correlation of the two EGIs in the Fourier domain and makes use of the spherical and rotational harmonic transforms. For pairs with low overlap which fail a critical verification step, the rotational alignment can be obtained by the alignment of constellation images generated from the EGIs. Rotationally aligned sets are matched by correlation using the Fourier transform of volumetric functions. A fine alignment is acquired in the final step by running Iterative Closest Points with just few iterations.
Determining of Camera Rotation from Vanishing Points of Lines on Horizontal Planes
Authors: Shigang Li 0001, Saburo Tsuji, Masakazu Imai
Venue: Conference ICCV 1990
Year: 1990

A novel method that uses vanishing points of horizontal lines, but not parallel to each other, for estimating the change of camera azimuth is proposed. Although indoor scenes contain many such lines, there also exist non-horizontal lines which should be discarded from the group of lines for estimating the azimuth change. Distinguishing non-horizontal lines from horizontal ones is difficult for a static image, but possible for the dynamic imagery because non-horizontal lines show different motion patterns as the camera moves.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Computing 3D Object Parts from Similarities among Object Views
Authors: Florin Cutzu
Venue: Conference CVPR 2000
Year: 2000

The following shape segmentation problem is addressed: find the part decomposition of a 3D object that accounts for an observed pattern of similarities among several of the object's views. This represents the inverse, ill-posed version of the direct problem of computing perceptual similarities among object views when the object parts are known. The problem is solved by inverting a proposed model for the direct similarity-from-parts problem by resorting to regularization techniques. The algorithm takes as input the geometry of the object (given as a triangular mesh), the camera positions corresponding to the rest views, and the perceptual similarities among the test views. The output of the algorithm is a segmentation of the surface of the object into connected regions, i.e., parts.
Web usage mining based on probabilistic latent semantic analysis
Authors: Xin Jin, Yanzan Zhou, Bamshad Mobasher
Venue: Conference KDD 2004
Year: 2004

The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of user sessions and discovering association rules or frequent navigational paths, do not generally provide the ability to automatically characterize or quantify the unobservable factors that lead to common navigational patterns. It is, therefore, necessary to develop techniques that can automatically discover hidden semantic relationships among users as well as between users and Web objects. Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among users and pages based on the co-occurrence patterns of these pages in user sessions. In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA. We show the flexibility of this framework in characterizing various relationships among users and Web objects. Since these relationships are measured in terms of probabilities, we are able to use probabilistic inference to perform a variety of analysis tasks such as user segmentation, page classification, as well as predictive tasks such as collaborative recommendations. We demonstrate the effectiveness of our approach through experiments performed on real-world data sets.
Document clustering via adaptive subspace iteration
Authors: Tao Li, Sheng Ma, Mitsunori Ogihara
Venue: Conference SIGIR 2004
Year: 2004

Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1 , which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.
Classifying large data sets using svms with hierarchical clusters
Authors: Hwanjo Yu, Jiong Yang, Jiawei Han
Venue: Conference KDD 2003
Year: 2003

Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, Clustering-Based SVM (CB-SVM), which is specifically designed for handling very large data sets. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CB-SVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CB-SVM is highly scalable for very large data sets while also generating high classification accuracy.
Dynammo: Mining and summarization of coevolving sequences with missing values
Authors: Lei Li, James McCann, Nancy S. Pollard, Christos Faloutsos
Venue: Conference KDD 2009
Year: 2009

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values.We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.
Linear laplacian discrimination for feature extraction
Authors: Deli Zhao, Zhouchen Lin, Rong Xiao 0003, Xiaoou Tang
Venue: Conference CVPR 2007
Year: 2007

Discriminant feature extraction plays a fundamental role in pattern recognition. In this paper, we propose the linear Laplacian discrimination (LLD) algorithm/or discriminant feature extraction. LLD is an extension of linear discriminant analysis (LDA). Our motivation is to address the issue that LDA cannot work well in cases where sample spaces are non-Euclidean. Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample spaces. Thus the structural information of classes is contained in the within-class and the between-class Laplacian matrices which are free from metrics of sample spaces. The optimal discriminant subspace can be derived by controlling the structural evolution of Laplacian matrices. Experiments are performed on the facial database for FRGC version 2. Experimental results show that LLD is effective in extracting discriminant features.
Bilevel sparse coding for coupled feature spaces
Authors: Jianchao Yang, Zhaowen Wang, Zhe Lin 0001, Xianbiao Shu, Thomas S. Huang
Venue: Conference CVPR 2012
Year: 2012

In this paper, we propose a bilevel sparse coding model for coupled feature spaces, where we aim to learn dictionaries for sparse modeling in both spaces while enforcing some desired relationships between the two signal spaces. We first present our new general sparse coding model that relates signals from the two spaces by their sparse representations and the corresponding dictionaries. The learning algorithm is formulated as a generic bilevel optimization problem, which is solved by a projected first-order stochastic gradient descent algorithm. This general sparse coding model can be applied to many specific applications involving coupled feature spaces in computer vision and signal processing. In this work, we tailor our general model to learning dictionaries for compressive sensing recovery and single image super-resolution to demonstrate its effectiveness. In both cases, the new sparse coding model remarkably outperforms previous approaches in terms of recovery accuracy.
Video summarization via transferrable structured learning
Authors: Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Yong Yu
Venue: Conference WWW 2011
Year: 2011

It is well-known that textual information such as video transcripts and video reviews can significantly enhance the performance of video summarization algorithms. Unfortunately, many videos on the Web such as those from the popular video sharing site YouTube do not have useful textual information. The goal of this paper is to propose a transfer learning framework for video summarization: in the training process both the video features and textual features are exploited to train a summarization algorithm while for summarizing a new video only its video features are utilized. The basic idea is to explore the transferability between videos and their corresponding textual information. Based on the assumption that video features and textual features are highly correlated with each other, we can transfer textual information into knowledge on summarization using video information only. In particular, we formulate the video summarization problem as that of learning a mapping from a set of shots of a video to a subset of the shots using the general framework of SVM-based structured learning. Textual information is transferred by encoding them into a set of constraints used in the structured learning process which tend to provide a more detailed and accurate characterization of the different subsets of shots. Experimental results show significant performance improvement of our approach and demonstrate the utility of textual information for enhancing video summarization.
A straw shows which way the wind blows: Ranking potentially popular items from early votes
Authors: Peifeng Yin, Ping Luo, Min Wang, Wang-Chien Lee
Venue: Conference WSDM 2012
Year: 2012

Prediction of popular items in online content sharing systems has recently attracted a lot of attention due to the tremendous need of users and its commercial values. Different from previous works that make prediction by fitting a popularity growth model, we tackle this problem by exploiting the latent conforming and maverick personalities of those who vote to assess the quality of on-line items. We argue that the former personality prompts a user to cast her vote conforming to the majority of the service community while on the contrary the later personality makes her vote different from the community. We thus propose a Conformer-Maverick (CM) model to simulate the voting process and use it to rank top-k potentially popular items based on the early votes they received. Through an extensive experimental evaluation, we validate our ideas and find that our proposed CM model achieves better performance than baseline solutions, especially for smaller k.
Scaling matrix factorization for recommendation with randomness
Authors: Lei Tang, Patrick Harrington
Venue: Conference WWW 2013
Year: 2013

Recommendation is one of the core problems in eCommerce. In our application, different from conventional collaborative filtering, one user can engage in various types of activities in a sequence. Meanwhile, the number of users and items involved are quite huge, entailing scalable approaches. In this paper, we propose one simple approach to integrate multiple types of user actions for recommendation. A two-stage randomized matrix factorization is presented to handle large-scale collaborative filtering where alternating least squares or stochastic gradient descent is not viable. Empirical results show that the method is quite scalable, and is able to effectively capture correlations between different actions, thus making more relevant recommendations.
A worst-case comparison between temporal difference and residual gradient with linear function approximation
Authors: Lihong Li 0001
Venue: Conference ICML 2008
Year: 2008

Residual gradient (RG) was proposed as an alternative to TD(0) for policy evaluation when function approximation is used, but there exists little formal analysis comparing them except in very limited cases. This paper employs techniques from online learning of linear functions and provides a worst-case (non-probabilistic) analysis to compare these two types of algorithms when linear function approximation is used. No statistical assumptions are made on the sequence of observations, so the analysis applies to non-Markovian and even adversarial domains as well. In particular, our results suggest that RG may result in smaller temporal differences, while TD(0) is more likely to yield smaller prediction errors. These phenomena can be observed even in two simple Markov chain examples that are non-adversarial.
Delta-reasoner: A semantic web reasoner for an intelligent mobile platform
Authors: Boris Motik, Ian Horrocks, Su Myeon Kim
Venue: Conference WWW 2012
Year: 2012

To make mobile device applications more intelligent, one can combine the information obtained via device sensors with background knowledge in order to deduce the user's current context, and then use this context to adapt the application's behaviour to the user's needs. In this paper we describe Delta-Reasoner, a key component of the Intelligent Mobile Platform (IMP), which was designed to support context-aware applications running on mobile devices. Context-aware applications and the mobile platform impose unusual requirements on the reasoner, which we have met by incorporating advanced features such as incremental reasoning and continuous query evaluation into our reasoner. Although we have so far been able to conduct only a very preliminary performance evaluation, our results are very encouraging: our reasoner exhibits sub-second response time on ontologies whose size significantly exceeds the size of the ontologies used in the IMP.
Context and hierarchy in a probabilistic image model
Authors: Ya Jin, Stuart Geman
Venue: Conference CVPR 2006
Year: 2006

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a "composition machine") for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a "Markov backbone."
Understanding the sparsity: Augmented matrix factorization with sampled constraints on unobservables
Authors: Yongfeng Zhang, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma
Venue: Conference CIKM 2014
Year: 2014

An important problem of matrix completion/approximation based on Matrix Factorization (MF) algorithms is the existence of multiple global optima; this problem is especially serious when the matrix is sparse, which is common in real-world applications such as personalized recommender systems. In this work, we clarify data sparsity by bounding the solution space of MF algorithms. We present the conditions that an MF algorithm should satisfy for reliable completion of the unobservables, and we further propose to augment current MF algorithms with extra constraints constructed by compressive sampling on the unobserved values, which is well-motivated by the theoretical analysis. Model learning and optimal solution searching is conducted in a properly reduced solution space to achieve more accurate and efficient rating prediction performances. We implemented the proposed algorithms in the Map-Reduce framework, and comprehensive experimental results on Yelp and Dianping datasets verified the effectiveness and efficiency of the augmented matrix factorization algorithms.
Tracking and modeling non-rigid objects with rank constraints
Authors: Lorenzo Torresani, Danny B. Yang, Eugene J. Alexander, Christoph Bregler
Venue: Conference CVPR 2001
Year: 2001

This paper presents a novel solution for flow-based tracking and 3D reconstruction of deforming objects in monocular image sequences. A non-rigid 3D object undergoing rotation and deformation can be effectively approximated using a linear combination of 3D basis shapes. This puts a bound on the rank of the tracking matrix. The rank constraint is used to achieve robust and precise low-level optical flow estimation without prior knowledge of the 3D shape of the object. The bound on the rank is also exploited to handle occlusion at the tracking level leading to the possibility of recovering the complete trajectories of occluded/disoccluded points. Following the same low-rank principle, the resulting flow matrix can be factored to get the 3D pose, configuration coefficients, and 3D basis shapes. The flow matrix is factored in an iterative manner, looping between solving for pose, configuration, and basis shapes. The flow-based tracking is applied to several video sequences and provides the input to the 3D non-rigid reconstruction task. Additional results on synthetic data and comparisons to ground truth complete the experiments.
Computational social indicators: a case study of chinese university ranking
Authors: Fuli Feng, Liqiang Nie, Xiang Wang, Richang Hong, Tat-Seng Chua
Venue: Conference SIGIR 2017
Year: 2017

Many professional organizations produce regular reports of social indicators to monitor social progress. Despite their reasonable results and societal value, early efforts on social indicator computing suffer from three problems: 1) labor-intensive data gathering, 2) insufficient data, and 3) expert-relied data fusion. Towards this end, we present a novel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data. For each channel, this scheme presents the semi-structured and unstructured data with simple graphs and hypergraphs, respectively. It then groups the channels into different clusters according to their correlations. After that, it uses a unified model to learn the cluster-wise common spaces, perform ranking separately upon each space, and fuse these rankings to produce the final one. We take Chinese university ranking as a case study and validate our scheme over a real-world dataset. It is worth emphasizing that our scheme is applicable to computation of other social indicators, such as Educational attainment.
Geoburst: Real-time local event detection in geo-tagged tweet streams
Authors: Chao Zhang, Guangyu Zhou, Quan Yuan, Honglei Zhuang, Yu Zheng, Lance M. Kaplan, Shaowen Wang, Jiawei Han
Venue: Conference SIGIR 2016
Year: 2016

The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities to it. That said, how to extract quality local events from geo-tagged tweet streams in real time remains largely unsolved so far.