Aggregating Local Image Descriptors into Compact Codes - Jegou, H.; Perronnin, F.; Douze, M.; Sanchez, J.; Perez, P.; Schmid, C. (IEEE transactions on pattern analysis and machine intelligence, 2012)
This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.