Keyframe extraction plays a significant role in wide variety of real-time video processing applications such as video summarization, video management and retrieval, etc. A keyframe captures the whole content of its shot and does not contain any redundant information. The keyframe extraction algorithms are facing challenges due to different visual characteristics in videos of different categories. Therefore, a single feature is not enough to capture visual characteristics of a variety of videos. In order to tackle this problem, we propose an approach of keyframe extraction that uses hybridization of features. In the present article, we propose a novel shot detection-based keyframe extraction algorithm based on combination of two features: one is Pearson correlation coefficient (PCC) and other is color moments (CM). The linear transformation invariance property of PCC facilitates the proposed algorithm to work well under varying lighting conditions. On the other hand, the scale and rotation invariance properties of color moments are beneficial for representation of complex objects that may be present in different poses and orientations. These sustained reasons support the combination of these two features, which brings significant benefits for keyframe extraction in the proposed method. The proposed method detects shot boundaries by employing combo feature set (PCC and CM). From each shot, the frame with highest mean and standard deviation is selected as keyframe. Furthermore, another important contribution is that we developed a new dataset by collecting the videos of different categories such as movies, news, serials, animations and personal interviews and made it available online. The proposed method is experimented on three datasets: two publicly available datasets and one dataset developed by us. The performance of the proposed method on these datasets has been evaluated on the basis of different evaluation parameters: figure of merit, detection percentage, accuracy, and missing factor. Principal advantage of proposed work lies in the fact that it is capable to detect both the abrupt and gradual shot transitions. In real-time videos, it is common to have abrupt and small transitions. The experimental results show the superior performance of the proposed method over the other state-of-the-art methods.

中文翻译：

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使用 Pearson 相关系数和颜色矩提取关键帧

关键帧提取在各种实时视频处理应用中发挥着重要作用，例如视频摘要、视频管理和检索等。关键帧捕获其镜头的全部内容，不包含任何冗余信息。由于不同类别视频的视觉特征不同，关键帧提取算法面临挑战。因此，单一特征不足以捕捉各种视频的视觉特征。为了解决这个问题，我们提出了一种使用特征混合的关键帧提取方法。在本文中，我们提出了一种新的基于镜头检测的关键帧提取算法，该算法基于两个特征的组合：一个是 Pearson 相关系数 (PCC)，另一个是颜色矩 (CM)。PCC 的线性变换不变性有助于所提出的算法在不同的光照条件下良好地工作。另一方面，颜色矩的尺度和旋转不变性有利于表示可能以不同姿势和方向出现的复杂对象。这些持续的原因支持了这两个特征的组合，这为所提出的方法中的关键帧提取带来了显着的好处。所提出的方法通过使用组合特征集（PCC 和 CM）来检测镜头边界。从每个镜头中，选择均值和标准差最高的帧作为关键帧。此外，另一个重要的贡献是我们通过收集不同类别的视频（如电影、新闻、连续剧、动画和个人访谈，并在线提供。所提出的方法在三个数据集上进行了实验：两个公开可用的数据集和一个我们开发的数据集。已根据不同的评估参数对所提出的方法在这些数据集上的性能进行了评估：品质因数、检测百分比、准确度和缺失因子。拟议工作的主要优势在于它能够检测突然和逐渐的镜头过渡。在实时视频中，出现突然和小的过渡是很常见的。实验结果表明，所提出的方法优于其他最先进的方法。已根据不同的评估参数对所提出的方法在这些数据集上的性能进行了评估：品质因数、检测百分比、准确度和缺失因子。拟议工作的主要优势在于它能够检测突然和逐渐的镜头过渡。在实时视频中，出现突然和小的过渡是很常见的。实验结果表明，所提出的方法优于其他最先进的方法。已根据不同的评估参数对所提出的方法在这些数据集上的性能进行了评估：品质因数、检测百分比、准确度和缺失因子。拟议工作的主要优势在于它能够检测突然和逐渐的镜头过渡。在实时视频中，出现突然和小的过渡是很常见的。实验结果表明，所提出的方法优于其他最先进的方法。突然和小的过渡是很常见的。实验结果表明，所提出的方法优于其他最先进的方法。突然和小的过渡是很常见的。实验结果表明，所提出的方法优于其他最先进的方法。