Real-time, large-scale duplicate image detection method based on multi-feature fusion

Recently, using old or irrelevant images in microblogs to spread false rumors has become increasingly rampant. Therefore, tracking and verifying the sources of images has become essential. In order to solve this problem, this paper provides a real-time, large-scale duplicate image detection method based on multi-feature fusion. This method firstly uses multi-feature fusion to improve retrieval accuracy. Then, by Hbase optimization, it uses a bloom filter and range query to improve retrieval efficiency. Experimental results show that, compared with existing algorithms, this method has higher precision and recall rates. Meanwhile, real-time responsiveness and scalability of the approach also meet real-world needs.