Memory efficient recognition of specific objects with local features

Balancing the recognition rate, processing time and memory requirement is an important issue for object recognition based on local features. For the task of recognizing not generic but specific objects (object instances), a larger number of local features enable us to improve the recognition rate but pose a problem of processing time and memory requirement. For the problem of processing time, approximate nearest neighbor search is known to be extremely effective. In this paper, we propose a method of memory reduction by applying scalar quantization to local features. From experimental results on 100,000 images, we have found that the scalar quantization is of great help; As compared to the original representation with 16 bit/dimension, the recognition rate of 98.1 % was kept unchanged using the representation with 2 bit/dim.