near-duplicate video retrieval current research and future trends pdf

Near-duplicate video retrieval current research and future trends pdf

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Near-duplicate video retrieval: Current research and future trends

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Aniket Sugandhi and Deepshikha Sharma. International Journal of Computer Applications 14 , July The information retrieval processes are playing essential role in the computer based database exploration or finding the essential contents from the databases. Now in these days a number of search techniques and retrieval models are exist by using which the users can find the data. According to the different data formats the information retrieval processes are also varying therefore different data format based retrieval process are works in different manner. In this presented work the content based video retrieval model is presented. In the content based video retrieval model the work is initiated from the segmentation of the videos into the set of frames.

Near-duplicate video retrieval: Current research and future trends

Emerging Internet services and applications attract increasing users to involve in diverse video-related activities, such as video searching, video downloading, video sharing and so on. As normal operations, they lead to an explosive growth of online video volume, and inevitably give rise to the massive near-duplicate contents. Near-duplicate video retrieval NDVR has always been a hot topic. The primary purpose of this paper is to present a comprehensive survey and an updated reviewof the advance on large-scaleNDVR to supply guidance for researchers. Specifically, we summarize and compare the definitions of near-duplicate videos NDVs in the literature, analyze the relationship between NDVR and its related research topics theoretically, describe its generic framework in detail, investigate the existing state-of-the-art NDVR systems. Finally, we present the development trends and research directions of this topic. Please wait a minute

Each flow vector is binned according to itssprimary angle from the horizontal axis and weightedsaccording to its magnitude. To accelerate the framessimilarity computation, keyframes containing similar visualsfeatures are clustered by K-means clustering, and eachscluster is assigned a unique symbol. Keyframes within asclu Videos have To find a copy ofsa query video in a video Computer Science and Information Technologies, Vol. Given asreference database and a query video,

As one of key technologies in content-based near-duplicate detection and video retrieval, video sequence matching can be used to judge whether two videos exist duplicate or near-duplicate segments or not. Despite a lot of research efforts devoted in recent years, how to precisely and efficiently perform sequence matching among videos which may be subject to complex audio-visual transformations from a large-scale database still remains a pretty challenging task. To address this problem, this paper proposes a multiscale video sequence matching MS-VSM method, which can gradually detect and locate the similar segments between videos from coarse to fine scales. At the coarse scale, it makes use of the Maximum Weight Matching MWM algorithm to rapidly select several candidate reference videos from the database for a given query. Then for each candidate video, its most similar segment with respect to the given query is obtained at the middle scale by the Constrained Longest Ascending Matching Subsequence CLAMS algorithm, and then can be used to judge whether that candidate exists near-duplicate or not.

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An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world.

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The exponential growth of online videos, along with the increasing user involvements to video-related activities, has been observed as a constant phenomenon during last decade. User's time spent on video capturing, editing, uploading, searching and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of a already-large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted lots of attention.

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ТРАНСТЕКСТ тогда еще не был создан, и принятие стандарта лишь облегчило бы процесс шифрования и значительно затруднило АНБ выполнение его и без того нелегкой задачи. Фонд электронных границ сразу увидел в этом конфликт интересов и всячески пытался доказать, что АНБ намеренно создаст несовершенный алгоритм - такой, какой ему будет нетрудно взломать. Чтобы развеять эти опасения, конгресс объявил, что, когда алгоритм будет создан, его передадут для ознакомления лучшим математикам мира, которые должны будут оценить его качество. Команда криптографов АНБ под руководством Стратмора без особого энтузиазма создала алгоритм, который окрестила Попрыгунчиком, и представила его в конгресс для одобрения. Зарубежные ученые-математики проверили Попрыгунчика и единодушно подтвердили его высокое качество.

 - Я там. У него случился инфаркт. Я сам. Никакой крови. Никакой пули. Беккер снисходительно покачал головой: - Иногда все выглядит не так, как есть на самом деле.

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