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121
Automated Extraction and Parameterization of Motions in Large Data Sets
 ACM Transactions on Graphics
, 2004
"... Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively ..."
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Cited by 183 (2 self)
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Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively parameterized space of motions. To find logically similar motions that are numerically dissimilar, our search method employs a novel distance metric to find “close ” motions and then uses them as intermediaries to find more distant motions. Search queries are answered at interactive speeds through a precomputation that compactly represents all possibly similar motion segments. Once a set of related motions has been extracted, we automatically register them and apply blending techniques to create a continuous space of motions. Given a function that defines relevant motion parameters, we present a method for extracting motions from this space that accurately possess new parameters requested by the user. Our algorithm extends previous work by explicitly constraining blend weights to reasonable values and having a runtime cost that is nearly independent of the number of example motions. We present experimental results on a test data set of 37,000 frames, or about ten minutes of motion sampled at 60 Hz.
Robust and fast similarity search for moving object trajectories
 In Proc. ACM SIGMOD Int. Conf. on Management of Data
, 2005
"... An important consideration in similaritybased retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, dis ..."
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Cited by 155 (14 self)
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An important consideration in similaritybased retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods. 1.
Indexing large humanmotion databases
 In Proc. 30th VLDB Conf
, 2004
"... Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of ..."
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Cited by 64 (6 self)
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Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of such data for general use and reuse is not yet a solved problem. Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and reordering of the data. Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis
Path similarity skeleton graph matching
 IEEE TRANS. PAMI
, 2008
"... This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the to ..."
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Cited by 53 (8 self)
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This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations.
Modeling Multiple Time Series for Anomaly Detection
 FIFTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING
, 2005
"... Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for reallife monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a re ..."
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Cited by 42 (0 self)
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Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for reallife monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.
Interactive Visual Clustering of Large Collections of Trajectories
 VAST
, 2009
"... One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multidimensional space of properties. Howeve ..."
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Cited by 37 (8 self)
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One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multidimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatiotemporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface.
Rotation invariant distance measures for trajectories
 Proceedings of SIGKDD
, 2004
"... For the discovery of similar patterns in 1D timeseries, it is very typical to perform a normalization of the data (for example a transformation so that the data follow a zero mean and unit standard deviation). Such transformations can reveal latent patterns and are very commonly used in datamining ..."
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Cited by 28 (2 self)
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For the discovery of similar patterns in 1D timeseries, it is very typical to perform a normalization of the data (for example a transformation so that the data follow a zero mean and unit standard deviation). Such transformations can reveal latent patterns and are very commonly used in datamining applications. However, when dealing with multidimensional timeseries, which appear naturally in applications such as videotracking, motioncapture etc, similar motion patterns can also be expressed at different orientations. It is therefore imperative to provide support for additional transformations, such as rotation. In this work, we transform the positional information of moving data, into a space that is translation, scale and rotation invariant. Our distance measure in the new space is able to detect elastic matches and can be efficiently lower bounded, thus being computationally tractable. The proposed methods are easy to implement, fast to compute and can have many applications for real world problems, in areas such as handwriting recognition and posture estimation in motioncapture data. Finally, we empirically demonstrate the accuracy and the efficiency of the technique, using real and synthetic handwriting data.
An Efficient and Accurate Method for Evaluating Time Series Similarity
, 2007
"... A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified ǫ threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, tim ..."
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Cited by 25 (1 self)
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A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified ǫ threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, time shifts, and data scaling. Our work proposes a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate such threshold value techniques, including LCSS and EDR. Using FTSE, we show that these techniques can be evaluated faster than using either traditional dynamic programming or even warprestricting methods such as the SakoeChiba band and the Itakura Parallelogram. We also show that FTSE can be used in a framework that can evaluate a richer range of ǫ thresholdbased scoring techniques, of which EDR and LCSS are just two examples. This framework, called Swale, extends the ǫ thresholdbased scoring techniques to include arbitrary match rewards and gap penalties. Through extensive empirical evaluation, we show that Swale can obtain greater accuracy than existing methods.
Querysensitive embeddings
 In ACM International Conference on Management of Data (SIGMOD). 706–717. ACM Transactions on Database Systems, Vol. ?, No. ?, ? 20?. · Vassilis Athitsos et al
"... A common problem in many types of databases is retrieving the most similar matches to a query object. Finding those matches in a large database can be too slow to be practical, especially in domains where objects are compared using computationally expensive similarity (or distance) measures. Embeddi ..."
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Cited by 24 (11 self)
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A common problem in many types of databases is retrieving the most similar matches to a query object. Finding those matches in a large database can be too slow to be practical, especially in domains where objects are compared using computationally expensive similarity (or distance) measures. Embedding methods can significantly speed up retrieval by mapping objects into a vector space, where distances can be measured rapidly using a Minkowski metric. In this paper we present a novel way to improve embedding quality. In particular, we propose to construct embeddings that use a “querysensitive ” distance measure for the target space of the embedding. This distance measure is used to compare the vectors that the query and database objects are mapped to. The term “querysensitive ” means that the distance measure changes depending on the current query object. We demonstrate theoretically that using a querysensitive distance measure increases the modeling power of embeddings and allows them to capture more of the structure of the original space. We also demonstrate experimentally that querysensitive embeddings can significantly improve retrieval performance. In experiments with an image database of handwritten digits and a timeseries database, the proposed method outperforms existing stateoftheart nonEuclidean indexing methods, meaning that it provides significantly better tradeoffs between efficiency and retrieval accuracy.
BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
, 2008
"... This paper describes BoostMap, a method for efficient nearest neighbor retrieval under computationally expensive distance measures. Database and query objects are embedded into a vector space in which distances can be measured efficiently. Each embedding is treated as a classifier that predicts for ..."
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Cited by 23 (5 self)
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This paper describes BoostMap, a method for efficient nearest neighbor retrieval under computationally expensive distance measures. Database and query objects are embedded into a vector space in which distances can be measured efficiently. Each embedding is treated as a classifier that predicts for any three objects X, A, B whether X is closer to A or to B. It is shown that a linear combination of such embeddingbased classifiers naturally corresponds to an embedding and a distance measure. Based on this property, the BoostMap method reduces the problem of embedding construction to the classical boosting problem of combining many weak classifiers into an optimized strong classifier. The classification accuracy of the resulting strong classifier is a direct measure of the amount of nearest neighbor structure preserved by the embedding. An important property of BoostMap is that the embedding optimization criterion is equally valid in both metric and nonmetric spaces. Performance is evaluated in databases of hand images, handwritten digits, and time series. In all cases, BoostMap significantly improves retrieval efficiency with small losses in accuracy compared to bruteforce search. Moreover, BoostMap significantly outperforms existing nearest neighbor retrieval methods such as Lipschitz embeddings, FastMap, and VPtrees.