1,890 research outputs found
Generalized residual vector quantization for large scale data
Vector quantization is an essential tool for tasks involving large scale
data, for example, large scale similarity search, which is crucial for
content-based information retrieval and analysis. In this paper, we propose a
novel vector quantization framework that iteratively minimizes quantization
error. First, we provide a detailed review on a relevant vector quantization
method named \textit{residual vector quantization} (RVQ). Next, we propose
\textit{generalized residual vector quantization} (GRVQ) to further improve
over RVQ. Many vector quantization methods can be viewed as the special cases
of our proposed framework. We evaluate GRVQ on several large scale benchmark
datasets for large scale search, classification and object retrieval. We
compared GRVQ with existing methods in detail. Extensive experiments
demonstrate our GRVQ framework substantially outperforms existing methods in
term of quantization accuracy and computation efficiency.Comment: published on International Conference on Multimedia and Expo 201
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a novel highly parallelizable re-ranking method for candidate vectors
by efficiently reusing already computed intermediate values. Due to its small
memory footprint during traversal, the method lends itself to an efficient,
parallel GPU implementation. This Product Quantization Tree (PQT) approach
significantly outperforms recent state of the art methods for high dimensional
nearest neighbor queries on standard reference datasets. Ours is the first work
that demonstrates GPU performance superior to CPU performance on high
dimensional, large scale ANN problems in time-critical real-world applications,
like loop-closing in videos
Pairwise Quantization
We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data
Scalable Image Retrieval by Sparse Product Quantization
Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional
feature indexing and retrieval is the crux of large-scale image retrieval. A
recent promising technique is Product Quantization, which attempts to index
high-dimensional image features by decomposing the feature space into a
Cartesian product of low dimensional subspaces and quantizing each of them
separately. Despite the promising results reported, their quantization approach
follows the typical hard assignment of traditional quantization methods, which
may result in large quantization errors and thus inferior search performance.
Unlike the existing approaches, in this paper, we propose a novel approach
called Sparse Product Quantization (SPQ) to encoding the high-dimensional
feature vectors into sparse representation. We optimize the sparse
representations of the feature vectors by minimizing their quantization errors,
making the resulting representation is essentially close to the original data
in practice. Experiments show that the proposed SPQ technique is not only able
to compress data, but also an effective encoding technique. We obtain
state-of-the-art results for ANN search on four public image datasets and the
promising results of content-based image retrieval further validate the
efficacy of our proposed method.Comment: 12 page
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