2,152 research outputs found
Learning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area
Variable-Length Hashing
Hashing has emerged as a popular technique for large-scale similarity search.
Most learning-based hashing methods generate compact yet correlated hash codes.
However, this redundancy is storage-inefficient. Hence we propose a lossless
variable-length hashing (VLH) method that is both storage- and
search-efficient. Storage efficiency is achieved by converting the fixed-length
hash code into a variable-length code. Search efficiency is obtained by using a
multiple hash table structure. With VLH, we are able to deliberately add
redundancy into hash codes to improve retrieval performance with little
sacrifice in storage efficiency or search complexity. In particular, we propose
a block K-means hashing (B-KMH) method to obtain significantly improved
retrieval performance with no increase in storage and marginal increase in
computational cost.Comment: 10 pages, 6 figure
A Survey on Learning to Hash
Nearest neighbor search is a problem of finding the data points from the
database such that the distances from them to the query point are the smallest.
Learning to hash is one of the major solutions to this problem and has been
widely studied recently. In this paper, we present a comprehensive survey of
the learning to hash algorithms, categorize them according to the manners of
preserving the similarities into: pairwise similarity preserving, multiwise
similarity preserving, implicit similarity preserving, as well as quantization,
and discuss their relations. We separate quantization from pairwise similarity
preserving as the objective function is very different though quantization, as
we show, can be derived from preserving the pairwise similarities. In addition,
we present the evaluation protocols, and the general performance analysis, and
point out that the quantization algorithms perform superiorly in terms of
search accuracy, search time cost, and space cost. Finally, we introduce a few
emerging topics.Comment: To appear in IEEE Transactions On Pattern Analysis and Machine
Intelligence (TPAMI
Rank Subspace Learning for Compact Hash Codes
The era of Big Data has spawned unprecedented interests in developing hashing
algorithms for efficient storage and fast nearest neighbor search. Most
existing work learn hash functions that are numeric quantizations of feature
values in projected feature space. In this work, we propose a novel hash
learning framework that encodes feature's rank orders instead of numeric values
in a number of optimal low-dimensional ranking subspaces. We formulate the
ranking subspace learning problem as the optimization of a piece-wise linear
convex-concave function and present two versions of our algorithm: one with
independent optimization of each hash bit and the other exploiting a sequential
learning framework. Our work is a generalization of the Winner-Take-All (WTA)
hash family and naturally enjoys all the numeric stability benefits of rank
correlation measures while being optimized to achieve high precision at very
short code length. We compare with several state-of-the-art hashing algorithms
in both supervised and unsupervised domain, showing superior performance in a
number of data sets.Comment: 10 page
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Unsupervised hashing can desirably support scalable content-based image
retrieval (SCBIR) for its appealing advantages of semantic label independence,
memory and search efficiency. However, the learned hash codes are embedded with
limited discriminative semantics due to the intrinsic limitation of image
representation. To address the problem, in this paper, we propose a novel
hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH).
The key idea is to \emph{directly} augment the semantics of discrete image hash
codes by exploring auxiliary contextual modalities. To this end, a unified
hashing framework is formulated to simultaneously preserve visual similarities
of images and perform semantic transfer from contextual modalities. Further, to
guarantee direct semantic transfer and avoid information loss, we explicitly
impose the discrete constraint, bit--uncorrelation constraint and bit-balance
constraint on hash codes. A novel and effective discrete optimization method
based on augmented Lagrangian multiplier is developed to iteratively solve the
optimization problem. The whole learning process has linear computation
complexity and desirable scalability. Experiments on three benchmark datasets
demonstrate the superiority of DSTH compared with several state-of-the-art
approaches
Unsupervised Cross-Media Hashing with Structure Preservation
Recent years have seen the exponential growth of heterogeneous multimedia
data. The need for effective and accurate data retrieval from heterogeneous
data sources has attracted much research interest in cross-media retrieval.
Here, given a query of any media type, cross-media retrieval seeks to find
relevant results of different media types from heterogeneous data sources. To
facilitate large-scale cross-media retrieval, we propose a novel unsupervised
cross-media hashing method. Our method incorporates local affinity and distance
repulsion constraints into a matrix factorization framework. Correspondingly,
the proposed method learns hash functions that generates unified hash codes
from different media types, while ensuring intrinsic geometric structure of the
data distribution is preserved. These hash codes empower the similarity between
data of different media types to be evaluated directly. Experimental results on
two large-scale multimedia datasets demonstrate the effectiveness of the
proposed method, where we outperform the state-of-the-art methods
Set-to-Set Hashing with Applications in Visual Recognition
Visual data, such as an image or a sequence of video frames, is often
naturally represented as a point set. In this paper, we consider the
fundamental problem of finding a nearest set from a collection of sets, to a
query set. This problem has obvious applications in large-scale visual
retrieval and recognition, and also in applied fields beyond computer vision.
One challenge stands out in solving the problem---set representation and
measure of similarity. Particularly, the query set and the sets in dataset
collection can have varying cardinalities. The training collection is large
enough such that linear scan is impractical. We propose a simple representation
scheme that encodes both statistical and structural information of the sets.
The derived representations are integrated in a kernel framework for flexible
similarity measurement. For the query set process, we adopt a learning-to-hash
pipeline that turns the kernel representations into hash bits based on simple
learners, using multiple kernel learning. Experiments on two visual retrieval
datasets show unambiguously that our set-to-set hashing framework outperforms
prior methods that do not take the set-to-set search setting.Comment: 9 page
Shared Predictive Cross-Modal Deep Quantization
With explosive growth of data volume and ever-increasing diversity of data
modalities, cross-modal similarity search, which conducts nearest neighbor
search across different modalities, has been attracting increasing interest.
This paper presents a deep compact code learning solution for efficient
cross-modal similarity search. Many recent studies have proven that
quantization-based approaches perform generally better than hashing-based
approaches on single-modal similarity search. In this paper, we propose a deep
quantization approach, which is among the early attempts of leveraging deep
neural networks into quantization-based cross-modal similarity search. Our
approach, dubbed shared predictive deep quantization (SPDQ), explicitly
formulates a shared subspace across different modalities and two private
subspaces for individual modalities, and representations in the shared subspace
and the private subspaces are learned simultaneously by embedding them to a
reproducing kernel Hilbert space, where the mean embedding of different
modality distributions can be explicitly compared. In addition, in the shared
subspace, a quantizer is learned to produce the semantics preserving compact
codes with the help of label alignment. Thanks to this novel network
architecture in cooperation with supervised quantization training, SPDQ can
preserve intramodal and intermodal similarities as much as possible and greatly
reduce quantization error. Experiments on two popular benchmarks corroborate
that our approach outperforms state-of-the-art methods
SNAP: A General Purpose Network Analysis and Graph Mining Library
Large networks are becoming a widely used abstraction for studying complex
systems in a broad set of disciplines, ranging from social network analysis to
molecular biology and neuroscience. Despite an increasing need to analyze and
manipulate large networks, only a limited number of tools are available for
this task.
Here, we describe Stanford Network Analysis Platform (SNAP), a
general-purpose, high-performance system that provides easy to use, high-level
operations for analysis and manipulation of large networks. We present SNAP
functionality, describe its implementational details, and give performance
benchmarks. SNAP has been developed for single big-memory machines and it
balances the trade-off between maximum performance, compact in-memory graph
representation, and the ability to handle dynamic graphs where nodes and edges
are being added or removed over time. SNAP can process massive networks with
hundreds of millions of nodes and billions of edges. SNAP offers over 140
different graph algorithms that can efficiently manipulate large graphs,
calculate structural properties, generate regular and random graphs, and handle
attributes and meta-data on nodes and edges. Besides being able to handle large
graphs, an additional strength of SNAP is that networks and their attributes
are fully dynamic, they can be modified during the computation at low cost.
SNAP is provided as an open source library in C++ as well as a module in
Python.
We also describe the Stanford Large Network Dataset, a set of social and
information real-world networks and datasets, which we make publicly available.
The collection is a complementary resource to our SNAP software and is widely
used for development and benchmarking of graph analytics algorithms
SHOE: Supervised Hashing with Output Embeddings
We present a supervised binary encoding scheme for image retrieval that
learns projections by taking into account similarity between classes obtained
from output embeddings. Our motivation is that binary hash codes learned in
this way improve both the visual quality of retrieval results and existing
supervised hashing schemes. We employ a sequential greedy optimization that
learns relationship aware projections by minimizing the difference between
inner products of binary codes and output embedding vectors. We develop a joint
optimization framework to learn projections which improve the accuracy of
supervised hashing over the current state of the art with respect to standard
and sibling evaluation metrics. We further boost performance by applying the
supervised dimensionality reduction technique on kernelized input CNN features.
Experiments are performed on three datasets: CUB-2011, SUN-Attribute and
ImageNet ILSVRC 2010. As a by-product of our method, we show that using a
simple k-nn pooling classifier with our discriminative codes improves over the
complex classification models on fine grained datasets like CUB and offer an
impressive compression ratio of 1024 on CNN features
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