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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Multi-label semantic concept detection in videos using fusion of asymmetrically trained deep convolutional neural networks and foreground driven concept co-occurrence matrix
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Multi-label semantic concept detection in videos using fusion of asymmetrically trained deep convolutional neural networks and foreground driven concept co-occurrence matrix

机译：使用非对称训练的深卷积神经网络和前景驱动概念共发生矩阵的视频中的多标签语义概念检测

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摘要

Describing visual contents in videos by semantic concepts is an effective and realistic approach that can be used in video applications such as annotation, indexing, retrieval and ranking. In these applications, video data needs to be labelled with some known set of labels or concepts. Assigning semantic concepts manually is not feasible due to the large volume of ever-growing video data. Hence, automatic semantic concept detection of videos is a hot research area. Recently Deep Convolutional Neural Networks (CNNs) used in computer vision tasks are showing remarkable performance. In this paper, we present a novel approach for automatic semantic video concept detection using deep CNN and foreground driven concept co-occurrence matrix (FDCCM) which keeps foreground to background concept co-occurrence values, built by exploiting concept co-occurrence relationship in pre-labelled TRECVID video dataset and from a collection of random images extracted from Google Images. To deal with the dataset imbalance problem, we have extended this approach by making a fusion of two asymmetrically trained deep CNNs and used FDCCM to further improve concept detection. The performance of the proposed approach is compared with state-of-the-art approaches for the video concept detection over the widely used TRECVID data set and is found to be superior to existing approaches.
机译：通过语义概念描述视频中的视觉内容是一种有效且现实的方法，可用于视频应用，例如注释，索引，检索和排序。在这些应用程序中，需要用一些已知的标签或概念标记视频数据。由于越来越大的视频数据，手动分配语义概念是不可行的。因此，自动语义概念检测视频是一个热门研究区域。最近在计算机视觉任务中使用的深度卷积神经网络（CNNS）呈现出显着的性能。在本文中，我们使用深CNN和前景驱动概念共生矩阵（FDCCM）介绍了一种自动语义视频概念检测的方法，该矩阵（FDCCM）将前景保持在背景概念共生存值，通过利用PRE之前利用概念共生关系构建。 - 标识的TRECVID视频数据集以及从Google映像中提取的随机图像集合。要处理数据集不平衡问题，我们通过融合了两个不对称培训的深CNN和使用FDCCM来扩展这种方法，以进一步改善概念检测。将所提出的方法的性能与视频概念检测的最先进方法进行比较，这些方法通过广泛使用的TRECVID数据集被发现，并且被发现优于现有方法。

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