Product sub-vector quantization for feature indexing - Pham, The-Anh; Le, Dinh-Nghiep; Nguyen, Thi-Lan-Phuong (Journal of Computer Science and Cybernetics, 2019)
This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of  underlying data while still maintaining reasonable memory allocation. In addition, the quantized data can be  jointly used with a clustering tree to perform approximate nearest search very efficiently. Experimental results demonstrate the superiority of the proposed method for different datasets in comparison with other methods.