A Powerful and Lightweight 3D Video Retrieval Using 3D Images Over Hadoop MapReduce

Abstract

Content Based Video Retrieval (CBVR) is an approach for redeeming 3D videos from several video sources such as YouTube, Viki etc. Various modern video retrieval applications are revealed by using approaches like key frame selection, features extraction, similarity matching, etc. We propound a new framework that gathers key frame selection, feature extraction, denoising and similarity matching for effective retrieval of videos from 3D images. We initiate Relative Entropy based Fast Key Frame Selection (REFKFS) for nominating optimal key frames for a video. BM3D filter with Bayesian threshold (BB) is proposed for decreasing white Gaussian noises on 3D key frames. We extract texture features, shape features, motion features and color features from the key frames. For extricating shape features, we prefer moments of objects and regions. After extricating features, we propose similarity matching process which is established from Multi-Featured Light-weight (MFLW) matching scheme for successful retrieval of videos with 3D images. Here we qualify Hadoop environment for handling massive sized database on video retrieval. Our experimental outcome includes larger database and furnish efficacious result as 98% of accuracy obtained for overall proposed framework.