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首页> 外文会议>International Conference on Soft Computing and Pattern Recognition >Improving Audiovisual Content Annotation Through a Semi-automated Process Based on Deep Learning
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Improving Audiovisual Content Annotation Through a Semi-automated Process Based on Deep Learning

机译：通过基于深度学习的半自动过程改善视听内容注释

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Over the last years, Deep Learning has become one of the most popular research fields of Artificial Intelligence. Several approaches have been developed to address conventional challenges of AI. In computer vision, these methods provide the means to solve tasks like image classification, object identification and extraction of features. In this paper, some approaches to face detection and recognition are presented and analyzed, in order to identify the one with the best performance. The main objective is to automate the annotation of a large dataset and to avoid the costy and time-consuming process of content annotation. The approach follows the concept of incremental learning and a R-CNN model was implemented. Tests were conducted with the objective of detecting and recognizing one personality within image and video content. Results coming from this initial automatic process are then made available to an auxiliary tool that enables further validation of the annotations prior to uploading them to the archive. Tests show that, even with a small size dataset, the results obtained are satisfactory.
机译：在过去几年中，深度学习已成为人工智能最受欢迎的研究领域之一。已经制定了几种方法来解决AI的传统挑战。在计算机愿景中，这些方法提供了解决图像分类，对象识别和特征提取等任务的方法。在本文中，提出和分析了一些面对检测和识别的方法，以识别具有最佳性能的方法。主要目标是自动化大型数据集的注释，并避免内容注释的昂贵和耗时过程。该方法遵循增量学习的概念，并实施了R-CNN模型。测试是通过检测和识别图像和视频内容中的一个个性的目的进行测试。然后，来自此初始自动进程的结果可供辅助工具可用，从而在将其上传到存档之前，可以进一步验证注释。测试表明，即使使用小尺寸数据集，所获得的结果也是令人满意的。

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