• P. Jagadeeswara Rao
• J.Prakruti
• G. Vamsi Krishna
• K.Sai Prathyusha
• M.Shiny

Approximate Nearest Neighbor (ANN) is a popular technique for searching using hashing and quantization methods. This is designed only for static databases. 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 project, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization[A] codebook that accommodates to the incoming streaming data. PQ model is both time-efficient and effective for ANN search in dynamic large-scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.

Subspaces, Codebook, Codeword, Clustering ,Quantization

"PRODUCT QUANTIZATION BASED APPROACH FOR APPROXIMATE NEAREST NEIGHBOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.505-508, March-2019, Available :http://www.jetir.org/papers/JETIR1903975.pdf

"PRODUCT QUANTIZATION BASED APPROACH FOR APPROXIMATE NEAREST NEIGHBOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp505-508, March-2019, Available at : http://www.jetir.org/papers/JETIR1903975.pdf