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Video Retrieval using Histogram and Sift Combined with Graph-based Image Segmentation | Science Publications

机译:直方图和筛分结合基于图的图像分割的视频检索科学出版物

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> Problem statement: Content-Based Video Retrieval (CBVR) is still an open hard problem because of the semantic gap between low-level features and high-level features, largeness of database, keyframe?s content, choosing feature.In this study we introduce a new approach for this problem based on Scale-Invariant Feature Transform (SIFT) feature, a new metric and an object retrieval method. Conclusion/Recommendations: Our algorithm is built on a Content-Based Image Retrieval (CBIR) method in which the keyframe database includes keyframes detected from video database by using our shot detection method. Experiments show that the approach of our algorithmhas fairly high accuracy.
机译: > 问题陈述:基于内容的视频检索(CBVR)仍然是一个开放的难题,因为低级功能和高级功能之间存在语义鸿沟,数据库庞大,关键帧的内容,选择特征。在这项研究中,我们介绍了一种基于尺度不变特征变换(SIFT)特征的新方法,新的度量标准和对象检索方法。 结论/建议:我们的算法基于基于内容的图像检索(CBIR)方法,其中关键帧数据库包含使用我们的镜头检测方法从视频数据库检测到的关键帧。实验表明,该算法具有较高的精度。

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