Aggregating and Searching frame in Video Using Semantic Analysis

A. Gadicha, M. Sarode, V. Thakare
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引用次数: 0

Abstract

The idea of Video content reclamation is a youthful field that has its genetics grounded forebears instinctive intelligence, numerical signal rectification, statistics, natural language understanding, If researchers are concentrating all these fast growing fields so none of these parental fields alone antiquated able to directly solve the retrieval problem. In this paper shows the path towards a step by step mechanism of CBVR i.e. analysis of entire video, video segmentation, key frames mining, feature extraction mining for retrieving the video from large video datasets. The proposed system inclination focuses on performing key frame mining using adaptive thresholding algorithm and canny mechanism for feature extraction purpose. In order to legalize this claim, content based video reclamation systems were furnished using color histogram, features extraction and different approaches are applied for the supervision of the semantic temperament of each frame in the video.
基于语义分析的视频帧聚合与搜索
视频内容回收的想法是一个年轻的领域,它有其遗传学基础的祖先本能智力,数字信号校正,统计学,自然语言理解,如果研究人员集中所有这些快速发展的领域,那么没有一个原始的领域能够直接解决检索问题。本文给出了CBVR逐步实现的机制,即整个视频分析、视频分割、关键帧挖掘、从大型视频数据集中检索视频的特征提取挖掘。提出的系统倾向于使用自适应阈值算法和canny机制进行关键帧挖掘,以达到特征提取的目的。为了使这种说法合法化,基于内容的视频回收系统使用颜色直方图,特征提取和不同的方法来监督视频中每帧的语义气质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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