Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-12900-1_12
DC FieldValue
dc.titleVideo repeat recognition and mining by visual features
dc.contributor.authorYang, X.
dc.contributor.authorTian, Q.
dc.date.accessioned2014-06-17T09:47:27Z
dc.date.available2014-06-17T09:47:27Z
dc.date.issued2010
dc.identifier.citationYang, X.,Tian, Q. (2010). Video repeat recognition and mining by visual features. Studies in Computational Intelligence 287 : 305-326. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-12900-1_12" target="_blank">https://doi.org/10.1007/978-3-642-12900-1_12</a>
dc.identifier.isbn9783642128998
dc.identifier.issn1860949X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/67347
dc.description.abstractRepeat video clips such as program logos and commercials are widely used in video productions, and mining them is important for video content analysis and retrieval. In this chapter we present methods to identify known and unknown video repeats respectively. For known video repeat recognition, we focus on robust feature extraction and classifier learning problems. A clustering model of visual features (e.g. color, texture) is proposed to represent video clip and subspace discriminative analysis is adopted to improve classification accuracy, which results in good results for short video clip recognition. We also propose a novel method to explore statistics of video database to estimate nearest neighbor classification error rate and learn the optimal classification threshold. For unknown video repeat mining, we address robust detection, searching efficiency and learning issues. Two detectors in a cascade structure are employed to efficiently detect unknown video repeats of arbitrary length, and this approach combines video segmentation, color fingerprinting, self-similarity analysis and Locality-Sensitive Hashing (LSH) indexing. A reinforcement learning approach is also adopted to efficiently learn optimal parameters. Experiment results show that very short video repeats and long ones can be detected with high accuracy. Video structure analysis by short video repeats mining is also presented in results. © 2010 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-12900-1_12
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentBIOENGINEERING
dc.description.doi10.1007/978-3-642-12900-1_12
dc.description.sourcetitleStudies in Computational Intelligence
dc.description.volume287
dc.description.page305-326
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.