Abstract
Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially when an immense volume of video content is being constantly generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream decoding. To validate our approach, we built a comprehensive data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our evaluations indicate precision, accuracy, and recall rates consistently above 80%, many exceeding 90%, and some reaching 99%. The algorithm operates approximately 15,000 times faster than real-time for 30fps videos, outperforming traditional Dynamic Time Warping (DTW) algorithm by seven orders of magnitude.
Abstract (translated)
将视频分类为不同的类别（如体育和音乐视频）对于多媒体理解和检索至关重要，尤其是在生成大量视频内容的情况下。传统方法需要对视频进行解压缩以提取像素级别的特征，如颜色、纹理和运动，从而增加计算和存储需求。此外，这些方法在低质量视频中常常存在性能退化。我们提出了一种新方法，只检查视频的后压缩比特流进行分类，无需进行比特流解码。为了验证我们的方法，我们构建了一个由29,000个YouTube视频片段组成的全集，总计6,000小时，跨越11个类别的综合数据集。我们的评估结果表明，精度、准确性和召回率均高于80%，许多甚至超过90%，有些达到99%。该算法在30fps视频上的运行速度约为实时速度的15,000倍，比传统动态时间膨胀（DTW）算法快7 orders of magnitude。
URL
https://arxiv.org/abs/2403.08580
