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Boundary Error Analysis and Categorization in the TRECVID News Story Segmentation Task

Published

Author(s)

Joaquim F. Arlandis, Paul D. Over, Wessel Kraaij

Abstract

In this paper, an error analysis based on boundary error popularity including semantic boundary categorization is applied in the context of the news story segmentation task from TRECVID, Clusters of systems were defined based on the input resources they used including video, audio and automatic speech recognition. A cross-popularity specific index was used to measure boundary error popularity across cluster, which allowed goal-driven selection of boundaries to be categorized. A wide set of boundaries was viewed and a summary of the error types is presented. this framework allowed drawing conclusions about the behavior of resource-based clusters in the context of news story segmentation.
Proceedings Title
Conference on Image + Video Retrieval
Conference Location
Undefined

Keywords

error analysis, story segmentation, TRECVID, video segmentation

Citation

Arlandis, J. , Over, P. and Kraaij, W. (2006), Boundary Error Analysis and Categorization in the TRECVID News Story Segmentation Task, Conference on Image + Video Retrieval, Undefined, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=150241 (Accessed May 22, 2024)

Issues

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Created February 12, 2006, Updated October 12, 2021