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Video concept detection using Hadoop MapReduce framework


Citation

Affendey, Lilly Suriani and Chamasemani, Fereshteh Falah and Ishak, Iskandar and Sidi, Fatimah (2014) Video concept detection using Hadoop MapReduce framework. In: Malaysian National Conference of Databases 2014 (MaNCoD 2014), 17 Sept. 2014, Universiti Putra Malaysia, Serdang, Selangor. (pp. 46-49). (Unpublished)

Abstract / Synopsis

Indexing video collection with a large set of video concepts allows us to employ a query-by-concept paradigm. Through concept detection, the task is to detect in the shot- segmented video data the presence of a set of pre-defined semantic concepts. Columbia University provided 374 detectors based on color, texture and edge features, and their unsupervised classifier fusion that can be utilized for concepts training purposes. We had successfully implemented a Concept-based Video retrieval System (CBVRS) to support query-by-concept. However, one of the main challenges is to reduce the training time of concepts to support the video concept detection. This paper presents an alternative architecture to overcome the issue. The proposed CBVRS framework, which consists of three main modules i.e. pre-processing, video analysis, and annotation module, shall utilize the Hadoop MapReduce framework for fast computation of the concept detection.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
Keywords: Video concept detection; Concept-based video retrieval; Semantic video annotation; Hadoop; MapReduce
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 20 May 2015 14:33
Last Modified: 29 Jul 2016 15:50
URI: http://psasir.upm.edu.my/id/eprint/38455
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