Online Product Quantization

Abstract

Online Product Quantization big data project report Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data.

Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model.

System Configuration:

H/W System Configuration:-

System          : Pentium I3 Processor.
Hard Disk       : 500 GB.
Monitor          : Standard LED Monitor
Input Devices : Keyboard
Ram               : 4 GB

S/W System Configuration:-

Operating system              : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database                          : MYSQL

Conclusion

In Online Product Quantization big data project report paper, we have presented our online PQ method to accommodate streaming data. In addition, we employ two budget constraints to facilitate partial codebook update to further alleviate the update time cost. A relative loss bound has been derived to guarantee the performance of our model. In addition, we propose an online PQ over sliding window approach, to emphasize on the real-time data. Experimental results show that our method is significantly faster in accommodating the streaming data, outperforms the competing online and batch hashing methods in terms of search accuracy and update time cost, and attains comparable search quality with batch mode PQ.