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.

翻译：将视频分类为不同类别（如体育和音乐视频）对于多媒体理解与检索至关重要，尤其是在海量视频内容持续生成的背景下。传统方法需要对视频进行解压缩以提取颜色、纹理、运动等像素级特征，从而增加了计算和存储需求。此外，这些方法在低质量视频中常出现性能下降问题。我们提出了一种新颖方法，仅通过分析视频压缩后的比特流即可完成分类，无需解码比特流。为验证该方法，我们构建了一个包含超过29,000个YouTube视频片段、总计6,000小时且涵盖11个类别的综合数据集。评估结果表明，精确率、准确率和召回率始终高于80%，多数超过90%，部分达到99%。该算法对30fps视频的处理速度约为实时处理的15,000倍，比传统动态时间规整（DTW）算法快七个数量级。