8 research outputs found
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
Spreading vectors for similarity search
Discretizing multi-dimensional data distributions is a fundamental step of
modern indexing methods. State-of-the-art techniques learn parameters of
quantizers on training data for optimal performance, thus adapting quantizers
to the data. In this work, we propose to reverse this paradigm and adapt the
data to the quantizer: we train a neural net which last layer forms a fixed
parameter-free quantizer, such as pre-defined points of a hyper-sphere. As a
proxy objective, we design and train a neural network that favors uniformity in
the spherical latent space, while preserving the neighborhood structure after
the mapping. We propose a new regularizer derived from the Kozachenko--Leonenko
differential entropy estimator to enforce uniformity and combine it with a
locality-aware triplet loss. Experiments show that our end-to-end approach
outperforms most learned quantization methods, and is competitive with the
state of the art on widely adopted benchmarks. Furthermore, we show that
training without the quantization step results in almost no difference in
accuracy, but yields a generic catalyzer that can be applied with any
subsequent quantizer.Comment: Published at ICLR 201
Reconfigurable Inverted Index
Existing approximate nearest neighbor search systems suffer from two
fundamental problems that are of practical importance but have not received
sufficient attention from the research community. First, although existing
systems perform well for the whole database, it is difficult to run a search
over a subset of the database. Second, there has been no discussion concerning
the performance decrement after many items have been newly added to a system.
We develop a reconfigurable inverted index (Rii) to resolve these two issues.
Based on the standard IVFADC system, we design a data layout such that items
are stored linearly. This enables us to efficiently run a subset search by
switching the search method to a linear PQ scan if the size of a subset is
small. Owing to the linear layout, the data structure can be dynamically
adjusted after new items are added, maintaining the fast speed of the system.
Extensive comparisons show that Rii achieves a comparable performance with
state-of-the art systems such as Faiss.Comment: ACMMM 2018 (oral). Code: https://github.com/matsui528/ri
Approximate search with quantized sparse representations
International audienceThis paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it. To this end, we propose to approximate database vectors by constrained sparse coding, where possible atom weights are restricted to belong to a finite subset. This formulation encompasses, as particular cases, previous state-of-the-art methods such as product or residual quantization. As opposed to traditional sparse coding methods, quantized sparse coding includes memory usage as a design constraint, thereby allowing us to index a large collection such as the BIGANN billion-sized benchmark. Our experiments , carried out on standard benchmarks, show that our formulation leads to competitive solutions when considering different trade-offs between learning/coding time, index size and search quality