Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern

Kazuhiro OTSUKA; Tsutomu HORIKOSHI; Haruhiko KOJIMA; Satoshi SUZUKI

Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern

Kazuhiro OTSUKA, Tsutomu HORIKOSHI, Haruhiko KOJIMA, Satoshi SUZUKI

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A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.

Publication: IEICE TRANSACTIONS on Information Vol.E83-D No.7 pp.1458-1465

Publication Date: 2000/07/25

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Type of Manuscript: Special Section PAPER (Special Issue on Machine Vision Applications)

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Kazuhiro OTSUKA, Tsutomu HORIKOSHI, Haruhiko KOJIMA, Satoshi SUZUKI, "Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern" in IEICE TRANSACTIONS on Information, vol. E83-D, no. 7, pp. 1458-1465, July 2000, doi: .
Abstract: A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_7_1458/_p

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@ARTICLE{e83-d_7_1458,
author={Kazuhiro OTSUKA, Tsutomu HORIKOSHI, Haruhiko KOJIMA, Satoshi SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern},
year={2000},
volume={E83-D},
number={7},
pages={1458-1465},
abstract={A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern
T2 - IEICE TRANSACTIONS on Information
SP - 1458
EP - 1465
AU - Kazuhiro OTSUKA
AU - Tsutomu HORIKOSHI
AU - Haruhiko KOJIMA
AU - Satoshi SUZUKI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2000
AB - A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.
ER -