Evaluation of GIST descriptors for web-scale image search 论文

摘要

The GIST descriptor has recently received increasing attention in the context of scene recognition. In this paper we evaluate the search accu-racy and complexity of the global GIST descriptor for two applications, for which a local description is usually preferred: same location/object recognition and copy detection. We identify the cases in which a global description can reasonably be used. The comparison is performed against a state-of-the-art bag-of-features representation. To evaluate the impact of GIST’s spatial grid, we compare GIST with a bag-of-features restricted to the same spatial grid as in GIST. Finally, we propose an indexing strategy for global descriptors that op-timizes the trade-off between memory usage and precision. Our scheme provides a reasonable accuracy in some widespread application cases to-gether with very high efficiency: In our experiments, querying an image database of 110 million images takes 0.18 second per image on a single ma-chine. For common copyright attacks, this efficiency is obtained without noticeably sacrificing the search accuracy compared with state-of-the-art approaches. 1