OpenRaiser/Intern-Atlas huggingface dataset:
Building segmentation for densely built urban regions using aerial lidar data
Authors: Bogdan C. Matei, Harpreet S. Sawhney, Supun Samarasekera, Janet Kim, Rakesh Kumar 0001
Venue: Conference CVPR 2008
Year: 2008

We present a novel building segmentation system for densely built areas, containing thousands of buildings per square kilometer. We employ solely sparse LIDAR (Light/Laser Detection Ranging) 3D data, captured from an aerial platform, with resolution less than one point per square meter. The goal of our work is to create segmented and delineated buildings as well as structures on top of buildings without requiring scanning for the sides of buildings. Building segmentation is a critical component in many applications such as 3D visualization, robot navigation and cartography. LIDAR has emerged in recent years as a more robust alternative to 2D imagery because it acquires 3D structure directly, without the shortcomings of stereo in un- textured regions and at depth discontinuities. Our main technical contributions in this paper are: (i) a ground segmentation algorithm which can handle both rural regions, and heavily urbanized areas, where the ground is 20% or less of the data, (ii) a building segmentation technique, which is robust to buildings in close proximity to each other, sparse measurements and nearby structured vegetation clutter, and (Hi) an algorithm for estimating the orientation of a boundary contour of a building, based on minimizing the number of vertices in a rectilinear approximation to the building outline, which can cope with significant quantization noise in the outline measurements. We have applied the proposed building segmentation system to several urban regions with areas of hundreds of square kilometers each, obtaining average segmentation speeds of less than three minutes per km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on a standard Pentium processor. Extensive qualitative results obtained by overlaying the 3D segmented regions onto 2D imagery indicate accurate performance of our system.
Focus in catadioptric imaging systems
Authors: Rahul Swaminathan
Venue: Conference ICCV 2007
Year: 2007

Catadioptric imaging systems consisting of conventional lens based cameras and mirrors are widely used in many vision applications. Most of the prior work in this area has focused either on aspects of mirror design or the application it is being used for. In contrast, little attention has been paid to the aspect of sharp image formation. In this paper we study how reflections in curved mirrors affect image formation from the perspective of lens focus. In particular we answer the question "How and where must the lens focus when imaging reflections in curved mirrors?". Using caustics to model the reflections, we show that the space of infinite scene depth is compressed within a finite volume which we call the "caustic volume". Analysis of the caustic volume is presented for common catadioptric systems. We also present a framework to derive the "optimal" focal plane at which to focus the lens for sharp image formation along with experimental verification.
Trajectory prediction: learning to map situations to robot trajectories
Authors: Nikolay Jetchev, Marc Toussaint
Venue: Conference ICML 2009
Year: 2009

Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a new situation. In effect, extensive data is accumulated containing situations together with the respective optimized trajectories - but this data is in practice hardly exploited. The aim of this paper is to learn from this data. Given a new situation we want to predict a suitable trajectory which only needs minor refinement by a conventional optimizer. Our approach has two essential ingredients. First, to generalize from previous situations to new ones we need an appropriate situation descriptor - we propose a sparse feature selection approach to find such well-generalizing features of situations. Second, the transfer of previously optimized trajectories to a new situation should not be made in joint angle space - we propose a more efficient task space transfer of old trajectories to new situations. Experiments on a simulated humanoid reaching problem show that we can predict reasonable motion prototypes in new situations for which the refinement is much faster than an optimization from scratch.
From images to scenes: Compressing an image cluster into a single scene model for place recognition
Authors: Edward Johns, Guang-Zhong Yang
Venue: Conference ICCV 2011
Year: 2011

The recognition of a place depicted in an image typically adopts methods from image retrieval in large-scale databases. First, a query image is described as a “bag-of-features” and compared to every image in the database. Second, the most similar images are passed to a geometric verification stage. However, this is an inefficient approach when considering that some database images may be almost identical, and many image features may not repeatedly occur. We address this issue by clustering similar database images to represent distinct scenes, and tracking local features that are consistently detected to form a set of real-world landmarks. Query images are then matched to landmarks rather than features, and a probabilistic model of landmark properties is learned from the cluster to appropriately verify or reject putative feature matches. We present novelties in both a bag-of-features retrieval and geometric verification stage based on this concept. Results on a database of 200K images of popular tourist destinations show improvements in both recognition performance and efficiency compared to traditional image retrieval methods.
Cooperation of visually guided behaviors
Authors: Jana Kosecka, Ruzena Bajcsy
Venue: Conference ICCV 1993
Year: 1993

The authors present modeling, analysis, and synthesis of visual behaviors of agents engaged in navigational tasks. They consider situations in which two agents can navigate independently or in cooperation. For the purpose of modeling the behaviors, a formalism is adopted from the discrete events systems (DES) theory that is suitable for investigating control-theoretic issues of a system. The focus is on the identification of elementary behaviors and their composition, leading to more complex behaviors. Two kinds of elementary behaviors are identified: one where observations are directly connected with actions, and one where observations and actions are either received or transmitted. The use of the DES formalism allows synthesis of complex behaviors in a systematic fashion and guarantees their controllability.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Effective Site Finding using Link Anchor Information
Authors: Nick Craswell, David Hawking, Stephen E. Robertson
Venue: Conference SIGIR 2001
Year: 2001

Link-based ranking methods have been described in the literature and applied in commercial Web search engines. However, according to recent TREC experiments, they are no better than traditional content-based methods. We conduct a different type of experiment, in which the task is to find the main entry point of a specific Web site. In our experiments, ranking based on link anchor text is twice as effective as ranking based on document content, even though both methods used the same BM25 formula. We obtained these results using two sets of 100 queries on a 18.5 million document set and another set of 100 on a 0.4 million document set. This site finding effectiveness begins to explain why many search engines have adopted link methods. It also opens a rich new area for effectiveness improvement, where traditional methods fail.
Indexing multi-dimensional time-series with support for multiple distance measures
Authors: Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh
Venue: Conference KDD 2003
Year: 2003

Although most time-series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of trajectory similarities. Trajectory datasets are very common in environmental applications, mobility experiments, video surveillance and are especially important for the discovery of certain biological patterns. Our primary similarity measure is based on the Longest Common Subsequence (LCSS) model, that offers enhanced robustness, particularly for noisy data, which are encountered very often in real world applications. However, our index is able to accommodate other distance measures as well, including the ubiquitous Euclidean distance, and the increasingly popular Dynamic Time Warping (DTW). While other researchers have advocated one or other of these similarity measures, a major contribution of our work is the ability to support all these measures without the need to restructure the index. Our framework guarantees no false dismissals and can also be tailored to provide much faster response time at the expense of slightly reduced precision/recall. The experimental results demonstrate that our index can help speed-up the computation of expensive similarity measures such as the LCSS and the DTW.
Margin based feature selection: Theory and algorithms
Authors: Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
Venue: Conference ICML 2004
Year: 2004

Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification problems the relevance is determined by the given labels on the training data. A good choice of features is a key for building compact and accurate classifiers. In this paper we introduce a margin based feature selection criterion and apply it to measure the quality of sets of features. Using margins we devise novel selection algorithms for multi-class classification problems and provide theoretical generalization bound. We also study the well known Relief algorithm and show that it resembles a gradient ascent over our margin criterion. We apply our new algorithm to various datasets and show that our new Simba algorithm, which directly optimizes the margin, outperforms Relief.
Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization
Authors: Furu Wei, Wenjie Li, Qin Lu, Yanxiang He
Venue: Conference SIGIR 2008
Year: 2008

Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.
AdaSum: an adaptive model for summarization
Authors: Jin Zhang, Xueqi Cheng, Gaowei Wu, Hongbo Xu
Venue: Conference CIKM 2008
Year: 2008

Topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. This paper proposes a novel adaptive model for summarization, AdaSum, under the assumption that the summary and the topic representation can be mutually boosted. AdaSum aims to simultaneously optimize the topic representation and extract effective summaries. This model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. Furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. We prove that the training process of AdaSum can enhance the performance measure used. Experimental results on DUC 2007 dataset show that AdaSum significantly outperforms the baseline methods for summarization (e.g. MRP, LexRank, and GSPS).
Mockup driven web development
Authors: Edward Benson
Venue: Conference WWW 2013
Year: 2013

Dynamic web development still borrows heavily from its origins in CGI scripts: modern web applications are largely designed and developed as programs that happen to output HTML. This thesis proposes to investigate the idea taking a mockup-centric approach instead, in which self-contained, full page web mockups are the central artifact driving the application development process. In some cases, these mockups are sufficient to infer the dynamic application structure completely.
Training deformable models for localization
Authors: Deva Ramanan, Cristian Sminchisescu
Venue: Conference CVPR 2006
Year: 2006

We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the likelihood of the part labels. Alternatively, one could fit a model such that, when the model is run on the training images, it finds the parts. We do this by maximizing the conditional likelihood of the training data. We formulate model-learning as parameter estimation in a conditional random field (CRF). Initializing parameters with their maximum likelihood estimates, we reach the global optimum by gradient ascent. We present a learning algorithm that searches exhaustively over all part locations in an image without relying on feature detectors. This provides millions of examples of training data, and seems to avoid over-fitting issues known with CRFs. Results for part localization are relatively scarce in the community. We present results on three established datasets; Caltech motorbikes [8], USC people [19], and Weizmann horses [3]. In the Caltech set we significantly outperform the state-of-the-art [6]. For the challenging people dataset, we present results that are comparable to [19], but are obtained using a significantly more generic model (devoid of a face or skin detector). Our model is general enough to find other articulated objects; we use it to recover poses of horses in the challenging Weizmann database.
Improving term weighting for community question answering search using syntactic analysis
Authors: David Carmel, Avihai Mejer, Yuval Pinter, Idan Szpektor
Venue: Conference CIKM 2014
Year: 2014

Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on community question answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.
Information retrieval in p2p networks using genetic algorithm
Authors: Wan Yeung Wong, Tak Pang Lau, Irwin King
Venue: Conference WWW 2005
Year: 2005

Hybrid Peer-to-Peer (P2P) networks based on the direct connection model have two shortcomings which are high bandwidth consumption and poor semi-parallel search. However, they can further be improved by the query propagation model. In this paper, we propose a novel query routing strategy called GAroute based on the query propagation model. By giving the current P2P network topology and relevance level of each peer, GAroute returns a list of query routing paths that cover as many relevant peers as possible. We model this as the Longest Path Problem in a directed graph which is NP-complete and we obtain high quality (0.95 in 100 peers) approximate solutions in polynomial time by using Genetic Algorithm (GA). We describe the problem modeling and proposed GA for finding long paths. Finally, we summarize the experimental results which measure the scalability and quality of different searching algorithms. According to these results, GAroute works well in some large scaled P2P networks.
Direct mining of discriminative and essential frequent patterns via model-based search tree
Authors: Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip S. Yu, Olivier Verscheure
Venue: Conference KDD 2008
Year: 2008

Frequent patterns provide solutions to datasets that do not have well-structured feature vectors. However, frequent pattern mining is non-trivial since the number of unique patterns is exponential but many are non-discriminative and correlated. Currently, frequent pattern mining is performed in two sequential steps: enumerating a set of frequent patterns, followed by feature selection. Although many methods have been proposed in the past few years on how to perform each separate step efficiently, there is still limited success in eventually finding highly compact and discriminative patterns. The culprit is due to the inherent nature of this widely adopted two-step approach. This paper discusses these problems and proposes a new and different method. It builds a decision tree that partitions the data onto different nodes. Then at each node, it directly discovers a discriminative pattern to further divide its examples into purer subsets. Since the number of examples towards leaf level is relatively small, the new approach is able to examine patterns with extremely low global support that could not be enumerated on the whole dataset by the two-step method. The discovered feature vectors are more accurate on some of the most difficult graph as well as frequent itemset problems than most recently proposed algorithms but the total size is typically 50% or more smaller. Importantly, the minimum support of some discriminative patterns can be extremely low (e.g. 0.03%). In order to enumerate these low support patterns, state-of-the-art frequent pattern algorithm either cannot finish due to huge memory consumption or have to enumerate 101 to 103 times more patterns before they can even be found. Software and datasets are available by contacting the author.
New algorithms for parking demand management and a city-scale deployment
Authors: Onno Zoeter, Christopher R. Dance, Stéphane Clinchant, Jean-Marc Andreoli
Venue: Conference KDD 2014
Year: 2014

On-street parking, just as any publicly owned utility, is used inefficiently if access is free or priced very far from market rates. This paper introduces a novel demand management solution: using data from dedicated occupancy sensors an iteration scheme updates parking rates to better match demand. The new rates encourage parkers to avoid peak hours and peak locations and reduce congestion and underuse. The solution is deliberately simple so that it is easy to understand, easily seen to be fair and leads to parking policies that are easy to remember and act upon. We study the convergence properties of the iteration scheme and prove that it converges to a reasonable distribution for a very large class of models. The algorithm is in use to change parking rates in over 6000 spaces in downtown Los Angeles since June 2012 as part of the LA Express Park project. Initial results are encouraging with a reduction of congestion and underuse, while in more locations rates were decreased than increased.
A markov clustering topic model for mining behaviour in video
Authors: Timothy M. Hospedales, Shaogang Gong, Tao Xiang 0002
Venue: Conference ICCV 2009
Year: 2009

This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robustness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into global behaviours, and correlates behaviours over time. A collapsed Gibbs sampler is derived for offline learning with unlabeled training data, and significantly, a new approximation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models, mining behaviours and detecting salient events in three complex and crowded public scenes.
Personalized recommendation via cross-domain triadic factorization
Authors: Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu
Venue: Conference WWW 2013
Year: 2013

Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models.