A Novel Clustering Based Near Duplicate Video Retrieval Model
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Abstract
There are various video related tools, such as video sharing, recording, marketing, and consulting. Ironically the video-related tasks are uploading, sharing, posting and searching the video content as per the need and requirement. There is an increase in the usage of video posting, sharing and uploading upon the social media for various purposes. The increase in the usage of near-duplicate videos (NDVs) created on the Internet are in various forms, such as simple reformatting, to specific purchases, transformations, editions, and mixtures of distinct impacts. Joint multi-view hashing is a promising alternative for translating videos into compact and low-dimensional binary codes, inspired by observing multiple retrieval models. With this form of hashing video storage and time usage in the recovery process is increased. The main purpose of this work is to build a novel clustering model to maintain multiple feature structures and to reduce the retrieval process's time consumption.
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