666 research outputs found
Locality sensitive hashing: a comparison of hash function types and querying mechanisms
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expensive. Dramatic performance gains are obtained using approximate search schemes, such as the popular Locality-Sensitive Hashing (LSH). Several extensions have been proposed to address the limitations of this algorithm, in particular, by choosing more appropriate hash functions to better partition the vector space. All the proposed extensions, however, rely on a structured quantizer for hashing, poorly fitting real data sets, limiting its performance in practice. In this paper, we compare several families of space hashing functions in a real setup, namely when searching for high-dimensional SIFT descriptors. The comparison of random projections, lattice quantizers, k-means and hierarchical k-means reveal that unstructured quantizer significantly improves the accuracy of LSH, as it closely fits the data in the feature space. We then compare two querying mechanisms introduced in the literature with the one originally proposed in LSH, and discuss their respective merits and limitations
Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition
Visual Place recognition is commonly addressed as an image retrieval problem.
However, retrieval methods are impractical to scale to large datasets, densely
sampled from city-wide maps, since their dimension impact negatively on the
inference time. Using approximate nearest neighbour search for retrieval helps
to mitigate this issue, at the cost of a performance drop. In this paper we
investigate whether we can effectively approach this task as a classification
problem, thus bypassing the need for a similarity search. We find that existing
classification methods for coarse, planet-wide localization are not suitable
for the fine-grained and city-wide setting. This is largely due to how the
dataset is split into classes, because these methods are designed to handle a
sparse distribution of photos and as such do not consider the visual aliasing
problem across neighbouring classes that naturally arises in dense scenarios.
Thus, we propose a partitioning scheme that enables a fast and accurate
inference, preserving a simple learning procedure, and a novel inference
pipeline based on an ensemble of novel classifiers that uses the prototypes
learned via an angular margin loss. Our method, Divide&Classify (D&C), enjoys
the fast inference of classification solutions and an accuracy competitive with
retrieval methods on the fine-grained, city-wide setting. Moreover, we show
that D&C can be paired with existing retrieval pipelines to speed up
computations by over 20 times while increasing their recall, leading to new
state-of-the-art results.Comment: Accepted to ICCV2
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