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首页> 外文期刊>Multimedia, IEEE Transactions on >Photo Stream Alignment and Summarization for Collaborative Photo Collection and Sharing
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Photo Stream Alignment and Summarization for Collaborative Photo Collection and Sharing

机译：照片流对齐和汇总，用于协作式照片收集和共享

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摘要

With the popularity of digital cameras and camera phones, it is common for different people, who may or may not know each other, to attend the same event and take pictures and videos from different spatial or personal perspectives. Within the realm of social media, it is desirable to enable these people to select and share their pictures and videos in order to enrich memories and facilitate social networking. However, it is cumbersome to manually manage these photos from different cameras, of which the clocks settings are often not calibrated. In this paper, we propose automatic algorithms to address the above problems. First, we accurately align different photo streams or sequences from different photographers for the same event in chronological order on a common timeline, while respecting the time constraints within each photo stream. Given the preferred similarity measures (e.g., visual, and spatial similarities), our algorithm performs photo stream alignment via matching on a bipartite kernel sparse representation graph that forces the data connections to be sparse in an explicit fashion. Furthermore, we can produce a summary master stream from the aligned super stream of photos for efficient sharing by removing those redundant photos in the super stream while accounting for the temporal integrity. Based on a similar kernel sparse representation graph, our master stream summarization algorithm performs greedy backward selection to drop redundant photos without affecting the integrity of remaining photos for the entire event. We evaluate our algorithms on real-world personal online albums for 36 events and demonstrate its efficacy in automatically facilitating collaborative photo collection and sharing.
机译：随着数码相机和照相手机的普及，彼此之间可能认识也可能不认识的人经常参加相同的活动并从不同的空间或个人角度拍摄照片和视频。在社交媒体领域内，期望使这些人能够选择并共享他们的图片和视频，以丰富记忆并促进社交网络。但是，手动管理来自不同相机的这些照片很麻烦，这些相机的时钟设置通常没有经过校准。在本文中，我们提出了自动算法来解决上述问题。首先，我们在同一时间轴上按时间顺序将同一事件的来自不同摄影师的不同照片流或序列准确对齐，同时注意每个照片流中的时间限制。给定首选的相似性度量（例如视觉和空间相似性），我们的算法通过在二分内核稀疏表示图上进行匹配来执行照片流对齐，以强制数据连接以显式方式稀疏。此外，我们可以从对齐的照片超级流中生成摘要主流，以便在考虑时间完整性的同时通过删除超级流中的那些多余照片来进行有效共享。基于类似的内核稀疏表示图，我们的主流摘要算法执行贪婪的向后选择，以删除多余的照片，而不会影响整个事件中其余照片的完整性。我们在现实世界的个人在线相册中评估了36个事件的算法，并证明了其在自动促进协作照片收集和共享方面的功效。

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