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首页> 外文期刊>Multimedia Tools and Applications >Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation
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Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation

机译：基于内核极端学习机的剧烈场景检测算法和梯度方向的三维直方图

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摘要

Most existing feature descriptors for video have limited representation ability. In order to improve the recognition accuracy of method for detecting the videos that include violent scenes and take advantage of the logical structure of video sequences, a novel feature constructing approach based on three dimensional histograms of gradient orientation (HOG3D), the Bag of Visual Words (BoVW) model, and feature pooling technology is proposed. This approach, combined with kernel extreme learning machine (KELM), can be used to detect violent scene. First, the HOG3D feature is extracted on the block level for video, and then the K-Means clustering algorithm is implemented to generate visual words. Then, the bag of visual words framework is used for the quantization of feature. And the feature pooling technology is operated to generate a feature vector for an entire video segment, and feature vectors of training data and testing data were used separately to train the model and evaluate the performance of the proposed approach. The experimental results showed that the proposed feature descriptor had good representation and generalization abilities. The proposed approach is efficient for violent scene detection, and the accuracy matches the best result on Hockey dataset, and it outperforms state-of-the-art on Movies.
机译：用于视频的大多数现有功能描述符都具有有限的表示能力。为了提高检测包括暴力场景的视频的识别准确性，并利用视频序列的逻辑结构，基于梯度方向的三维直方图的新特征构造方法，视觉词组（BOVW）模型，并提出了特征池技术。这种方法与内核极端学习机（KELM）相结合，可用于检测暴力场景。首先，在块电平上提取Hog3D功能以进行视频，然后实现K-Means群集算法以生成视觉单词。然后，用于视觉单词框架的袋用于量化特征。该功能池技术被操作以为整个视频段生成特征向量，并且培训数据和测试数据的特征向量被单独使用，以培训模型并评估所提出的方法的性能。实验结果表明，所提出的特征描述符具有良好的表示和泛化能力。所提出的方法对于暴力场景检测是有效的，并且精度与曲棍球数据集的最佳结果匹配，并且在电影上占据最先进的结果。

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