  This paper addresses the construction of a short-vector (128D) image
representation for large-scale image and particular object retrieval. In
particular, the method of joint dimensionality reduction of multiple
vocabularies is considered. We study a variety of vocabulary generation
techniques: different k-means initializations, different descriptor
transformations, different measurement regions for descriptor extraction. Our
extensive evaluation shows that different combinations of vocabularies, each
partitioning the descriptor space in a different yet complementary manner,
results in a significant performance improvement, which exceeds the
state-of-the-art.
