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300
SimilarityBased Queries for Time Series Data
 Proc. 1997 ACMSIGMOD Conf
, 1997
"... We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on timeseries data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a q ..."
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Cited by 136 (6 self)
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We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on timeseries data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a query processing algorithm that uses the underlying Rtree index of a multidimensional data set to answer similarity queries efficiently. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We relate our transformations to the general framework for similarity queries of Jagadish et al. 1
A Probabilistic Framework for Matching Temporal Trajectories: CondensationBased . . .
, 1998
"... The recognition of human gestures and facial expressions in image sequences is an important and challenging problem that enables a host of humancomputer interaction applications. This paper describes a framework for incremental recognition of human motion that extends the "Condensation" algorit ..."
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Cited by 107 (4 self)
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The recognition of human gestures and facial expressions in image sequences is an important and challenging problem that enables a host of humancomputer interaction applications. This paper describes a framework for incremental recognition of human motion that extends the "Condensation" algorithm proposed by Isard and Blake (ECCV'96). Humnan motions are
Determining the Similarity of Deformable Shapes
 Vision Research
, 1995
"... We study how to measure the degree of similarity between two image contours. We propose an approach for comparing contours that takes into account deformations in object shape, the articulation of parts, and variations in the shape and size of portions of objects. Our method uses dynamic programming ..."
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Cited by 105 (7 self)
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We study how to measure the degree of similarity between two image contours. We propose an approach for comparing contours that takes into account deformations in object shape, the articulation of parts, and variations in the shape and size of portions of objects. Our method uses dynamic programming to compute the minimum cost of bringing one shape into the other via local deformations. Using this as a starting point, we investigate the properties that such a cost function should have to model human performance and to perform usefully in a computer vision system. We suggest novel conditions on this cost function that help capture the partbased nature of objects without requiring any explicit decomposition of shapes into their parts. We then suggest several possible cost functions based on different physical models of contours, and describe experiments with these costs. 1 Introduction Detecting similarity is a key tool in interpretating images. In this paper we develop a measure of s...
Word Image Matching Using Dynamic Time Warping
, 2002
"... Libraries and other institutions are interested in providing access to scanned versions of their large collections of handwritten historical manuscripts on electronic media. Convenient access to a collection requires an index, which is manually created at great labour and expense. Since current hand ..."
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Cited by 94 (13 self)
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Libraries and other institutions are interested in providing access to scanned versions of their large collections of handwritten historical manuscripts on electronic media. Convenient access to a collection requires an index, which is manually created at great labour and expense. Since current handwriting recognizers do not perform well on historical documents, a technique called word spotting has been developed: clusters with occurrences of the same word in a collection are established using image matching. By annotating "interesting" clusters, an index can be built automatically. We present an algorithm for matching handwritten words in noisy historical documents. The segmented word images are preprocessed to create sets of 1dimensional features, which are then compared using dynamic time warping. We present experimental results on two different data sets from the George Washington collection. Our experiments show that this algorithm performs better and is faster than competing matching techniques.
Creating HighLevel Components with a Generative Representation for BodyBrain Evolution
, 2002
"... One of the main limitations of scalability in bodybrain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translatio ..."
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Cited by 93 (19 self)
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One of the main limitations of scalability in bodybrain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a nongenerative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows
Finding Similar Time Series
, 1996
"... . Similarity of objects is one of the crucial concepts in several applications, including data mining. For complex objects, similarity is nontrivial to define. In this paper we present an intuitive model for measuring the similarity between two time series. The model takes into account outliers, dif ..."
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Cited by 87 (9 self)
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. Similarity of objects is one of the crucial concepts in several applications, including data mining. For complex objects, similarity is nontrivial to define. In this paper we present an intuitive model for measuring the similarity between two time series. The model takes into account outliers, different scaling functions, and variable sampling rates. Using methods from computational geometry, we show that this notion of similarity can be computed in polynomial time. Using statistical approximation techniques, the algorithms can be speeded up considerably. We give preliminary experimental results that show the naturalness of the notion. 1 Introduction Being able to measure the similarity or dissimilarity between objects is a crucial point in many data retrieval and data mining applications; see [9] for a general discussion on similarity queries. For complex objects, defining similarity is by no means trivial. In this paper we consider the problem of defining the similarity between ti...
Structure Comparison and Structure Patterns
 JOURNAL OF COMPUTATIONAL BIOLOGY
, 1999
"... This article investigate different aspects regarding pairwise and multiple structure comparison, and the problem of automatically discover common patterns in a set of structures. Descriptions and representation of structures and patterns are investigated, as well as scoring and algorithms for com ..."
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Cited by 83 (2 self)
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This article investigate different aspects regarding pairwise and multiple structure comparison, and the problem of automatically discover common patterns in a set of structures. Descriptions and representation of structures and patterns are investigated, as well as scoring and algorithms for comparison and discovery. A framework and nomenclature is developed, and a lot of methods are reviewed and placed into this framework.
Searching Genetic Databases on Splash 2
 Proceedings of IEEE Workshop on FPGAs for Custom Computing Machines
, 1993
"... In this paper, we describe two systolic arrays for computing the edit distance between two genetic sequences using a wellknown dynamic programming algorithm. The systolic arrays have been implemented for the Splash 2 programmable logic array, and are intended to be used for database searching. Simu ..."
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Cited by 76 (0 self)
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In this paper, we describe two systolic arrays for computing the edit distance between two genetic sequences using a wellknown dynamic programming algorithm. The systolic arrays have been implemented for the Splash 2 programmable logic array, and are intended to be used for database searching. Simulations indicate that that the faster Splash 2 implementation can search a database at a rate of 12 million characters per second, several orders of magnitude faster than implementations of the dynamic programming algorithm on conventional computers. 1 Introduction With the onset of the Human Genome Initiative [1] and constant advances in genetic sequencing technology, genetic sequence data are being generated at an ever increasing rate 1 . As a result, biologists are faced with an influx of new sequences that they would like to classify and study by comparing them to existing databases. The analysis of a newly generated sequence typically involves searching the database for similar sequen...
On the Learnability and Usage of Acyclic Probabilistic Finite Automata
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 1995
"... We propose and analyze a distribution learning algorithm for a subclass of Acyclic Probabilistic Finite Automata (APFA). This subclass is characterized by a certain distinguishability property of the automata's states. Though hardness results are known for learning distributions generated by general ..."
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Cited by 71 (3 self)
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We propose and analyze a distribution learning algorithm for a subclass of Acyclic Probabilistic Finite Automata (APFA). This subclass is characterized by a certain distinguishability property of the automata's states. Though hardness results are known for learning distributions generated by general APFAs, we prove that our algorithm can efficiently learn distributions generated by the subclass of APFAs we consider. In particular, we show that the KLdivergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made arbitrarily small with high confidence in polynomial time. We present two applications of our algorithm. In the first, we show how to model cursively written letters. The resulting models are part of a complete cursive handwriting recognition system. In the second application we demonstrate how APFAs can be used to build multiplepronunciation models for spoken words. We evaluate the APFA based pronunciation models...
The String Edit Distance Matching Problems with Moves
, 2006
"... The edit distance between two strings S and R is defined to be the minimum number of character inserts, deletes and changes needed to convert R to S. Given a text string t of length n, and a pattern string p of length m, informally, the string edit distance matching problem is to compute the smalles ..."
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Cited by 58 (3 self)
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The edit distance between two strings S and R is defined to be the minimum number of character inserts, deletes and changes needed to convert R to S. Given a text string t of length n, and a pattern string p of length m, informally, the string edit distance matching problem is to compute the smallest edit distance between p and substrings of t. We relax the problem so that (a) we allow an additional operation, namely, substring moves, and (b) we allow approximation of this string edit distance. Our result is a near linear time deterministic algorithm to produce a factor of O(log n log ∗ n) approximation to the string edit distance with moves. This is the first known significantly subquadratic algorithm for a string edit distance problem in which the distance involves nontrivial alignments. Our results are obtained by embedding strings into L1 vector space using a simplified parsing technique we call Edit