Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
This work addresses the challenge of efficient image search for large-scale applications, though it appears incremental as it builds on existing vocabulary-based methods.
The paper tackles the problem of constructing short-vector image representations for large-scale image and object retrieval by using joint dimensionality reduction of multiple vocabularies, resulting in a significant performance improvement that exceeds the state-of-the-art.
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.