Improving Cluster-Based Index Structure for Approximate Nearest Neighbor Graph Search by Deep Learning-Based Hill-Climbing

This study presents a novel approach to archive an excellent tradeoff between search accuracy and computation cost in approximate nearest neighbor search. Usually, the k-nearest neighbor (kNN) graph and hill-climbing algorithm are adopted to accelerate the search process. However, using random seeds in the original hill-climbing is inefficient as they initiate an unsuitable search with inappropriate sources. Instead, we propose a neural network model to generate high-quality seeds that can boost query assignment efficiency. We evaluated the experiment on the benchmarks of SIFT1M and GIST1M datasets and showed the proposed seed prediction model effectively improves the search performance.