Evaluation of GIST descriptors for web-scale image search

The GIST descriptor has recently received increasing attention in the context of scene recognition. In this paper we evaluate the search accuracy 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 optimizes the trade-off between memory usage and precision. Our scheme provides a reasonable accuracy in some widespread application cases together with very high efficiency: In our experiments, querying an image database of 110 million images takes 0.18 second per image on a single machine. For common copyright attacks, this efficiency is obtained without noticeably sacrificing the search accuracy compared with state-of-the-art approaches.