Fast Video Retrieval under Sparse Training Data

Feature selection for video retrieval applications is impractical with existing techniques, because of their high time complexity and their failure on the relatively sparse training data that is available given video data size. In this paper we present a novel heuristic method for selecting image fe...

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Bibliographische Detailangaben

Hauptverfasser:	Liu, Yan, Kender, John R.
Format:	Buchkapitel
Sprache:	eng
Schlagworte:	Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Feature Selection Feature Selection Algorithm Feature Selection Method Feature Subset Pattern recognition. Digital image processing. Computational geometry Video Retrieval
Online-Zugang:	Volltext
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