Efficient searches for similar subsequences of different lengths in sequence databases

Park, S.; Chu, Wesley W.; Yoon, Jeehee; Hsu, Chung-Hsing

doi:10.1109/icde.2000.839384

Cited by 105 publications

(76 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To cope with this major problem, it is necessary to apply adaptive feature matching approaches [10,13] with appropriate correlation index. Dynamic Time Warping (DTW) [3,7] is a much more robust distance measurement method for time series, allowing similar shapes to match even if they are out of phase in the time axis. This fact clearly is shown in Figure 8.…”

Section: Feature Matching and Final Deciesionmentioning

confidence: 99%

Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Bahaghighat¹,

Sahba²,

Tehrani³

2012

IJCA

View full text Add to dashboard Cite

This paper gives a novel approach of automatic speaker recognition technology, with an emphasis on text-dependent speaker recognition. Speaker recognition has been studied actively for several decades. In fact, Speaker recognition system may be viewed as working in four stages, namely, analysis, feature extraction, modeling and testing. After some preprocessing modules, we apply MFCC, as one of the most important feature extraction methods in this field of works, to speech signals independently in order to extract feature vectors. Afterwards, obtained vectors are used by training system to find codewords for ten users in our Persian database by LBG VQ. Finally, we use DTW technique for recognizing a speaker among all. Our experience strongly indicates that the identification rate over 96% can be achieved by the proposed algorithm. General TermsSpeeches analyze,Speaker recognition.

show abstract

Section: Feature Matching and Final Deciesionmentioning

confidence: 99%

Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Bahaghighat¹,

Sahba²,

Tehrani³

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…The utility of uniform scaling has been noted before [9,14,15]. However, all previous work has focused on speeding up similarity search, when the scaling factor is known.…”

Section: Related Workmentioning

confidence: 99%

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases

Keogh

2003

Knowledge Discovery in Databases: PKDD 2003

View full text Add to dashboard Cite

Abstract. The problem of efficiently finding patterns in massive time series databases has attracted great interest, and, at least for the Euclidean distance measure, may now be regarded as a solved problem. However in recent years there has been an increasing awareness that Euclidean distance is inappropriate for many real world applications. 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 before calculating the Euclidean distance. However, DTW can only address the problem of local scaling. As we demonstrate in this work, uniform scaling may be just as important in many domains, including applications as diverse as bioinformatics, space telemetry monitoring and motion editing for computer animation. In this work, we demonstrate a novel technique to speed up similarity search under uniform scaling. As we will demonstrate, our technique is simple and intuitive, and can achieve a speedup of 2 to 3 orders of magnitude under realistic settings.

show abstract

“…In order to measure the similarity, each motion of query trajectory can be permitted to map with more than one motions of data trajectory. [4][5] The consideration 1 is generally needed for supporting an approximation matching from similar sub-trajectory retrieval and the considerations 2 and 3 are needed for improving the effectiveness of the approximation matching. In addition, the considerations 2 and 3 are very sensitive, depending on application areas.…”

Section: Related Workmentioning

confidence: 99%

Similarity Search Algorithm for Efficient Sub-trajectory Matching in Moving Databases

Lim

Shim

2007

Computational Science – ICCS 2007

View full text Add to dashboard Cite

Abstract. Similarity measure scheme on moving objects has become a topic of increasing in the area of moving databases. In this paper, we propose a new similarity search algorithm for efficient sub-trajectory matching. For measuring similarity between two sub-trajectories, we propose a new v(variable)-warping distance algorithm which enhances the existing time warping distance algorithm by permitting up to v replications for an arbitrary motion of a query trajectory. Our v-warping distance algorithm provides an approximate matching between two trajectories as well as an exact matching between them. Based on our vwarping distance algorithm, we also present a similarity measure scheme for the single trajectory in moving databases. Finally, we show that our scheme based on the v-warping distance achieves much better performance than other conventional schemes, such as Li's one (no-warping) and Shan's one (infinitewarping) in terms of precision and recall measures.

show abstract

Efficient searches for similar subsequences of different lengths in sequence databases

Cited by 105 publications

References 10 publications

Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Text-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases

Similarity Search Algorithm for Efficient Sub-trajectory Matching in Moving Databases

Contact Info

Product

Resources

About