Shot boundary detection with sparse presentation

Cai, Pingping; Guan, Yue; Xu, Ding; Zang, Yu

doi:10.1109/icsp.2016.7877960

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…TRECVid contains many subsets of video test data and ground truth information targeting the shot boundary detection which are widely used to evaluate the performance of different shot boundary detection algorithms [33]. Significant advancements are made by [34], [35], [36], [37], [38] [39], [40] to perform shot boundary detection to achieve optimal accuracy. Different accuracy ranges have been reported on TRECVid dataset from 94% to 96%.…”

Section: Related Workmentioning

confidence: 99%

Content-based Automatic Video Genre Identification

Shamsi¹,

Daudpota²,

Shaikh³

2019

IJACSA

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Video content is evolving enormously with the heavy usage of internet and social media websites. Proper searching and indexing of such video content is a major challenge. The existing video search potentially relies on the information provided by the user, such as video caption, description and subsequent comments on the video. In such case, if users provide insufficient or incorrect information about the video genre, the video may not be indexed correctly and ignored during search and retrieval. This paper proposes a mechanism to understand the contents of video and categorize it as Music Video, Talk Show, Movie/Drama, Animation and Sports. For video classification, the proposed system uses audio and visual features like audio signal energy, zero crossing rate, spectral flux from audio and shot boundary, scene count and actor motion from video. The system is tested on popular Hollywood, Bollywood and YouTube videos to give an accuracy of 96%.

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