38 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
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
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding
SUBIC: A supervised, structured binary code for image search
For large-scale visual search, highly compressed yet meaningful
representations of images are essential. Structured vector quantizers based on
product quantization and its variants are usually employed to achieve such
compression while minimizing the loss of accuracy. Yet, unlike binary hashing
schemes, these unsupervised methods have not yet benefited from the
supervision, end-to-end learning and novel architectures ushered in by the deep
learning revolution. We hence propose herein a novel method to make deep
convolutional neural networks produce supervised, compact, structured binary
codes for visual search. Our method makes use of a novel block-softmax
non-linearity and of batch-based entropy losses that together induce structure
in the learned encodings. We show that our method outperforms state-of-the-art
compact representations based on deep hashing or structured quantization in
single and cross-domain category retrieval, instance retrieval and
classification. We make our code and models publicly available online.Comment: Accepted at ICCV 2017 (Spotlight