Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing and Retrieval of On-line Handwritten Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Improving efficiency and effectiveness of dynamic time warping in large time series databases
Improving efficiency and effectiveness of dynamic time warping in large time series databases
Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Shape-based template matching for time series data
Knowledge-Based Systems
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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Multimedia data is ubiquitous and is involved in almost every aspect of our lives. Likewise, much of the world's data is in the form of time series, and as will be shown, many other types of data, such as video, image, and handwriting, can be transformed into time series. This fact has fueled enormous interest in time series retrieval in the database and data mining community. However, much of this work's narrow focus on efficiency and scalability has come at the cost of usability and effectiveness. In this work, we explore the utility of the multimedia data transformation into a much simpler one-dimensional time series representation. With this time series data, we can exploit the capability of Dynamic Time Warping, which results in a more accurate retrieval. We can also use a general framework that learns a distance measure with arbitrary constraints on the warping path of the Dynamic Time Warping calculation for both classification and query retrieval tasks. In addition, incorporating a relevance feedback system and query refinement into the retrieval task can further improve the precision/recall to a great extent.