With the advancement and dominant service of Internet videos, the content-based video deduplication system becomes an essential and dependent infrastructure for Internet video service. However, the explosively growing video data on the Internet challenges the system design and implementation for its scalability in several ways. (1) Although the quantization-based indexing techniques are effective for searching visual features at a large scale, the costly re-training over the complete dataset must be done periodically. (2) The high-dimensional vectors for visual features demand increasingly large SSD space, degrading I/O performance. (3) Videos crawled from the Internet are diverse, and visually similar videos are not necessarily the duplicates, increasing deduplication complexity. (4) Most videos are edited ones. The duplicate contents are more likely discovered as clips inside the videos, demanding processing techniques with close attention to details.
To address above-mentioned issues, we propose Maze, a full-fledged video deduplication system. Maze has an ANNS layer that indexes and searches the high dimensional feature vectors. The architecture of the ANNS layer supports efficient reads and writes and eliminates the data migration caused by re-training. Maze adopts the CNN-based feature and the ORB feature as the visual features, which are optimized for the specific video deduplication task. The features are compact and fully reside in the memory. Acoustic features are also incorporated in Maze so that the visually similar videos but having different audio tracks are recognizable. A clip-based matching algorithm is developed to discover duplicate contents at a fine granularity. Maze has been deployed as a production system for two years. It has indexed 1.3 billion videos and is indexing ~800 thousand videos per day. For the ANNS layer, the average read latency is 4 seconds and the average write latency is at most 4.84 seconds. The re-training over the complete dataset is no longer required no matter how many new data sets are added, eliminating the costly data migration between nodes. Maze recognizes the duplicate live streaming videos with both the similar appearance and the similar audio at a recall of 98%. Most importantly, Maze is also cost-effective. For example, the compact feature design helps save 5800 SSDs and the computation resources devoted to running the whole system decrease to 250K standard cores per billion videos.
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Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning
Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style-invariant matching, and can be used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artwork, we also demonstrate improvement on localization on the Oxford5K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.
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- Award ID(s):
- 1633310
- NSF-PAR ID:
- 10111413
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2163-6648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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https://aix.eng.usf.edu/research_automated_ethogramming.html
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
